
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

import random, re
import time
import warnings
warnings.filterwarnings("ignore")

# nlp libraries
import nltk
nltk.download('punkt')
nltk.download('stopwords')

from nltk.tokenize import word_tokenize
from tqdm import tqdm
from nltk.corpus import stopwords
stop_words = stopwords.words('english')
from sklearn.feature_extraction.text import TfidfVectorizer

from wordcloud import WordCloud

from gensim.models import Word2Vec
from gensim.models import KeyedVectors
from gensim.scripts.glove2word2vec import glove2word2vec

from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import LabelEncoder
from sklearn.decomposition import PCA

from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import RidgeClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, recall_score, precision_score
from sklearn.utils import resample
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GridSearchCV,RandomizedSearchCV
from xgboost.sklearn import XGBClassifier

pd.options.display.max_columns = None
pd.options.display.max_rows = None

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!


# mounting the drive
from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).


#load the data from xlsl file
data = pd.read_excel('drive/My Drive/AIML/Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx')


print("Shape of Data Provided : \n")
print("Number of Columns : ",data.shape[1])
print("Number of Rows : ",data.shape[0])

Shape of Data Provided : 

Number of Columns :  11
Number of Rows :  425


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 425 entries, 0 to 424
Data columns (total 11 columns):
 #   Column                    Non-Null Count  Dtype         
---  ------                    --------------  -----         
 0   Unnamed: 0                425 non-null    int64         
 1   Data                      425 non-null    datetime64[ns]
 2   Countries                 425 non-null    object        
 3   Local                     425 non-null    object        
 4   Industry Sector           425 non-null    object        
 5   Accident Level            425 non-null    object        
 6   Potential Accident Level  425 non-null    object        
 7   Genre                     425 non-null    object        
 8   Employee or Third Party   425 non-null    object        
 9   Critical Risk             425 non-null    object        
 10  Description               425 non-null    object        
dtypes: datetime64[ns](1), int64(1), object(9)
memory usage: 36.6+ KB


data.head()


# Remove 'Unnamed: 0' column from Data frame
data.drop("Unnamed: 0", axis=1, inplace=True)
data.head()


# Rename 'Data', 'Countries', 'Genre', 'Employee or Third Party' columns in Data frame
data.rename(columns={'Data':'Date', 'Countries':'Country', 'Genre':'Gender', 'Employee or Third Party':'EmployeeType'}, inplace=True)
data.head()


#check missing values in data
data.isnull().sum()


#checking for duplicate rows
data.duplicated().sum()

7


data.drop_duplicates(inplace=True)
data=data.reset_index(drop=True)
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 10 columns):
 #   Column                    Non-Null Count  Dtype         
---  ------                    --------------  -----         
 0   Date                      418 non-null    datetime64[ns]
 1   Country                   418 non-null    object        
 2   Local                     418 non-null    object        
 3   Industry Sector           418 non-null    object        
 4   Accident Level            418 non-null    object        
 5   Potential Accident Level  418 non-null    object        
 6   Gender                    418 non-null    object        
 7   EmployeeType              418 non-null    object        
 8   Critical Risk             418 non-null    object        
 9   Description               418 non-null    object        
dtypes: datetime64[ns](1), object(9)
memory usage: 32.8+ KB


#going through the unique values of each column. Description is more free text, so will have all unique almost. Omitting the description column alone
for col in data.columns:
    if col != 'Description':
        print("\nColumn : ",col)
        print("Unique Values : \n",data[col].value_counts())

Column :  Date
Unique Values : 
 Date
2017-02-08    6
2016-06-16    5
2016-05-04    4
2016-06-08    4
2016-10-03    4
2016-02-15    4
2017-06-11    4
2016-06-24    3
2016-12-12    3
2017-04-11    3
2016-03-22    3
2016-03-31    3
2017-05-06    3
2017-02-13    3
2017-02-17    3
2016-02-24    3
2017-05-16    3
2017-02-25    3
2016-09-20    3
2017-01-17    3
2017-04-25    3
2016-04-30    3
2016-03-18    3
2017-02-07    2
2016-06-18    2
2016-06-07    2
2017-01-31    2
2017-02-23    2
2016-06-05    2
2016-07-08    2
2016-05-26    2
2016-05-25    2
2016-05-22    2
2017-02-26    2
2017-02-27    2
2016-05-12    2
2016-05-11    2
2016-05-05    2
2016-06-03    2
2017-01-09    2
2016-07-07    2
2016-12-13    2
2016-11-08    2
2016-10-18    2
2016-10-13    2
2016-10-10    2
2016-11-23    2
2016-09-22    2
2016-09-21    2
2016-12-01    2
2016-12-08    2
2016-09-13    2
2016-09-06    2
2016-09-04    2
2016-12-15    2
2017-01-24    2
2016-08-30    2
2016-12-23    2
2016-08-24    2
2016-12-22    2
2016-08-14    2
2016-08-11    2
2016-08-01    2
2016-08-02    2
2017-01-05    2
2017-01-08    2
2017-03-09    2
2017-01-20    2
2017-03-08    2
2016-10-29    2
2016-03-16    2
2017-06-22    2
2016-02-04    2
2016-03-13    2
2017-04-23    2
2016-03-06    2
2017-06-09    2
2016-03-14    2
2016-02-21    2
2016-03-10    2
2017-06-24    2
2017-06-20    2
2016-03-30    2
2016-04-01    2
2016-03-02    2
2016-04-22    2
2017-04-07    2
2016-04-16    2
2016-04-11    2
2017-03-15    2
2016-02-16    2
2017-07-04    2
2017-03-25    2
2016-04-05    2
2016-04-14    2
2016-03-11    2
2017-04-04    2
2017-05-23    2
2017-06-04    1
2017-05-19    1
2017-05-30    1
2017-05-18    1
2016-12-16    1
2016-12-26    1
2016-12-28    1
2016-12-30    1
2017-06-14    1
2016-12-10    1
2016-12-19    1
2017-03-10    1
2017-06-15    1
2016-12-09    1
2016-11-04    1
2017-07-06    1
2016-11-11    1
2017-07-05    1
2016-11-13    1
2017-06-29    1
2016-11-19    1
2016-11-21    1
2017-06-19    1
2016-11-25    1
2016-11-28    1
2016-11-29    1
2017-06-23    1
2017-05-17    1
2016-11-30    1
2017-06-18    1
2017-06-17    1
2016-12-31    1
2017-01-12    1
2017-01-02    1
2017-05-10    1
2017-04-13    1
2017-04-08    1
2017-02-09    1
2017-04-10    1
2017-02-14    1
2017-04-06    1
2017-02-15    1
2017-02-16    1
2017-04-05    1
2016-10-27    1
2017-03-31    1
2017-03-01    1
2017-03-22    1
2017-03-02    1
2017-03-04    1
2017-03-18    1
2017-03-06    1
2017-04-12    1
2017-02-05    1
2017-02-04    1
2017-01-14    1
2017-05-05    1
2017-04-30    1
2017-01-06    1
2017-01-07    1
2017-04-29    1
2017-04-28    1
2017-01-10    1
2017-04-24    1
2017-02-01    1
2017-01-21    1
2017-01-23    1
2017-04-19    1
2017-01-25    1
2017-01-27    1
2017-01-29    1
2017-01-28    1
2016-01-01    1
2016-09-02    1
2016-10-26    1
2016-02-27    1
2016-03-04    1
2016-03-05    1
2016-03-09    1
2016-03-12    1
2016-03-17    1
2016-03-19    1
2016-03-25    1
2016-04-03    1
2016-04-02    1
2016-03-24    1
2016-04-04    1
2016-04-07    1
2016-04-08    1
2016-04-15    1
2016-04-17    1
2016-04-18    1
2016-04-21    1
2016-04-23    1
2016-04-26    1
2016-04-28    1
2016-04-29    1
2016-05-01    1
2016-05-02    1
2016-05-03    1
2016-05-14    1
2016-03-03    1
2016-02-26    1
2016-05-19    1
2016-02-29    1
2016-01-06    1
2016-01-08    1
2016-01-10    1
2016-01-12    1
2016-01-16    1
2016-01-17    1
2016-01-19    1
2016-01-26    1
2016-01-28    1
2016-01-30    1
2016-02-01    1
2016-02-02    1
2016-02-06    1
2016-02-07    1
2016-02-08    1
2016-02-25    1
2016-02-09    1
2016-02-10    1
2016-02-14    1
2016-02-13    1
2016-02-17    1
2016-02-19    1
2016-02-20    1
2016-02-18    1
2016-02-22    1
2016-05-17    1
2016-05-18    1
2016-10-24    1
2016-07-30    1
2016-08-12    1
2016-08-15    1
2016-08-18    1
2016-08-19    1
2016-08-22    1
2016-08-25    1
2016-08-29    1
2016-08-27    1
2016-09-01    1
2016-01-02    1
2016-09-03    1
2016-09-05    1
2016-09-12    1
2016-09-15    1
2016-09-17    1
2016-09-16    1
2016-09-27    1
2016-09-29    1
2016-09-30    1
2016-10-01    1
2016-10-04    1
2016-10-08    1
2016-10-11    1
2016-10-20    1
2016-10-23    1
2016-08-04    1
2016-07-29    1
2016-05-20    1
2016-07-27    1
2016-05-24    1
2016-05-27    1
2016-06-01    1
2016-06-02    1
2016-06-04    1
2016-06-10    1
2016-06-13    1
2016-06-17    1
2016-06-19    1
2016-06-21    1
2016-06-22    1
2016-06-23    1
2016-06-29    1
2016-07-02    1
2016-07-04    1
2016-07-09    1
2016-07-10    1
2016-07-11    1
2016-07-14    1
2016-07-15    1
2016-07-16    1
2016-07-18    1
2016-07-20    1
2016-07-21    1
2016-07-23    1
2017-07-09    1
Name: count, dtype: int64

Column :  Country
Unique Values : 
 Country
Country_01    248
Country_02    129
Country_03     41
Name: count, dtype: int64

Column :  Local
Unique Values : 
 Local
Local_03    89
Local_05    59
Local_01    56
Local_04    55
Local_06    46
Local_10    41
Local_08    27
Local_02    23
Local_07    14
Local_12     4
Local_09     2
Local_11     2
Name: count, dtype: int64

Column :  Industry Sector
Unique Values : 
 Industry Sector
Mining    237
Metals    134
Others     47
Name: count, dtype: int64

Column :  Accident Level
Unique Values : 
 Accident Level
I      309
II      40
III     31
IV      30
V        8
Name: count, dtype: int64

Column :  Potential Accident Level
Unique Values : 
 Potential Accident Level
IV     141
III    106
II      95
I       45
V       30
VI       1
Name: count, dtype: int64

Column :  Gender
Unique Values : 
 Gender
Male      396
Female     22
Name: count, dtype: int64

Column :  EmployeeType
Unique Values : 
 EmployeeType
Third Party             185
Employee                178
Third Party (Remote)     55
Name: count, dtype: int64

Column :  Critical Risk
Unique Values : 
 Critical Risk
Others                                       229
Pressed                                       24
Manual Tools                                  20
Chemical substances                           17
Cut                                           14
Venomous Animals                              13
Projection                                    13
Bees                                          10
Fall                                           9
Vehicles and Mobile Equipment                  8
Fall prevention (same level)                   7
remains of choco                               7
Pressurized Systems                            7
Fall prevention                                6
Suspended Loads                                6
Blocking and isolation of energies             3
Pressurized Systems / Chemical Substances      3
Power lock                                     3
Liquid Metal                                   3
Machine Protection                             2
Electrical Shock                               2
Poll                                           1
Individual protection equipment                1
Projection/Manual Tools                        1
Burn                                           1
Electrical installation                        1
Projection/Choco                               1
Projection/Burning                             1
Plates                                         1
Confined space                                 1
Traffic                                        1
\nNot applicable                               1
Projection of fragments                        1
Name: count, dtype: int64


#let us split the date column into day, month, year and day of week
data['Date'] = pd.to_datetime(data['Date'])
data['Year'] = data.Date.apply(lambda x : x.year)
data['Month'] = data.Date.apply(lambda x : x.month)
data['Day'] = data.Date.apply(lambda x : x.day)
data['WeekDay'] = data.Date.apply(lambda x : x.day_name())


# function to create month variable into seasons
def seasons(x):
    if x in [9, 10, 11]:
        season = 'Spring'
    elif x in [12, 1, 2]:
        season = 'Summer'
    elif x in [3, 4, 5]:
        season = 'Autumn'
    elif x in [6, 7, 8]:
        season = 'Winter'
    return season


data['Season'] = data['Month'].apply(seasons)
data.head()


def plot(x, df, type, y=""):
  plt.figure(figsize=(6, 6))
  title = "Distribution of " + x
  plt.title(x)
  if y != "" and type == "bar":
    sns.countplot(x =x, data = df, hue =y)
  elif y == "" and type == "bar":
    sns.countplot(x =x, data = df)
  elif type == "pie":
    category_counts = df[x].value_counts()
    plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', startangle=90)
    plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle
  plt.show()


#Plotting dependent variables - Pie chart
for col in data.columns:
    if col not in ['Description', 'Date', 'Local', 'Day', 'Accident Level', 'Potential Accident Level', 'Critical Risk']:
        print(col)
        plot(col, data, "pie")

Country

Industry Sector

Gender

EmployeeType

Year

Month

WeekDay

Season


#Critical risk column
plt.figure(figsize=(25, 6))
ax = sns.countplot(y='Critical Risk', data=data, palette="Reds")
for container in ax.containers:
    ax.bar_label(container)
#plt.xticks(rotation=20)
plt.title("Distribution of Critical Risk")
plt.show()


fig = plt.figure(figsize=(10,10))
ax = sns.countplot(data=data, x='Local')
for p in ax.patches:
   height = p.get_height()
   ax.text(p.get_x() + p.get_width() / 2.,
           height + 3,
           '{:1.0f}'.format(height),
           ha="center", va="bottom")

labels = list(data['Local'].value_counts().index)
#handles = [plt.Rectangle((0, 0), 1, 1, color=sns.color_palette()[i]) for i in range(len(labels))]
#plt.legend(handles, labels, loc='upper right', title="Category Count");


#Target variable distribution
for col in data.columns:
    if col in ['Accident Level', 'Potential Accident Level']:
        print(col)
        plot(col, data, "pie")

Accident Level

Potential Accident Level


plot("Industry Sector", data, "bar", "Country")


# Create a crosstab of Critical Risk and Country
crit_risk_cntry_table = pd.crosstab(index=data['Critical Risk'], columns=data['Country'])
#plot
crit_risk_cntry_table.plot(kind='bar', figsize=(8, 8), stacked=True)

plt.title("Proportion of Critical Risk in Different Countries")
plt.xlabel("Critical Risk")
plt.ylabel("Count")
plt.show()


plot("Accident Level", data, "bar", "Country")


acc_cntry_table = pd.crosstab(index=data['Accident Level'], columns=data['Country'])
acc_cntry_table


from scipy.stats import chi2_contingency
# Hypothesis Testing for Accident Levels by Country

# H0​: The proportions of accident levels does not differ in different countries.
# Ha​: The proportions of accident levels differ in different countries.

# Test Statistic: Chi-squared test

chi2, p, dof, expected = chi2_contingency(acc_cntry_table)

print("Chi-squared Statistic:", chi2)
print("p-value:", p)

reject_null = p < 0.05
print("Reject Null Hypothesis?:", reject_null)

if reject_null:
    print("We reject the null hypothesis. We have enough evidence to prove that the accident level distribution differs significantly across countries.")
else:
    print("We fail to reject the null hypothesis. We do not have enough evidence to prove that the accident level distribution differs significantly across countries.")

Chi-squared Statistic: 16.539177604348506
p-value: 0.03528174940919624
Reject Null Hypothesis?: True
We reject the null hypothesis. We have enough evidence to prove that the accident level distribution differs significantly across countries.


plot("Accident Level", data, "bar", "Industry Sector")


plot("Potential Accident Level", data, "bar", "Industry Sector")


plot("Accident Level", data, "bar", "Gender")


plot("Potential Accident Level", data, "bar", "Gender")


plt.figure(figsize=(15,10))
sns.countplot(data=data, x='Local', hue='Accident Level')
plt.show()


plt.figure(figsize=(10,10))
sns.countplot(data=data, x='Local', hue='Country')
plt.show()


country_cosstab= pd.crosstab(data["Country"], data["Local"])
country_cosstab


plot("Industry Sector", data, "bar", "Accident Level")


plot("Month", data, "bar", "Accident Level")


date_crosstab = pd.crosstab(data["Month"], data["Year"])
date_crosstab.plot(kind='line')
plt.xlabel('Month')
plt.ylabel('Number of cases')

Text(0, 0.5, 'Number of cases')


# Calculate the correlation matrix
le = LabelEncoder()
df_encoder = data.apply(le.fit_transform)

plt.figure(figsize=(12,12))
plt.title('Correlation_Matrix', fontsize=20)
sns.heatmap(df_encoder.corr(), square=True, cmap='coolwarm', annot=True, linewidth=0.2);


#Chi-Square test of Independence
# Hypothesis Testing for Accident Level and  Potential Accident Level

 #H0​: Accident Level and Potential accident level are not related.
 #Ha​: Accident Level and Potential accident level are related.

crosstab = pd.crosstab(data["Accident Level"], data["Potential Accident Level"])
crosstab


chi2, p, dof, expected = chi2_contingency(crosstab)
print("Chi-squared Statistic:", chi2)
print("p-value:", p)
print("degree of freedom", dof)
reject_null = p < 0.05
print("Reject Null Hypothesis?:", reject_null)

if reject_null:
    print("We reject the null hypothesis. We have enough evidence to prove that the accident level and potential accident level are dependent.")
else:
    print("We fail to reject the null hypothesis. We do not have enough evidence to prove that the accident level distribution and potential accident level are not dependent.")

Chi-squared Statistic: 249.45260461536674
p-value: 1.4728348366116975e-41
degree of freedom 20
Reject Null Hypothesis?: True
We reject the null hypothesis. We have enough evidence to prove that the accident level and potential accident level are dependent.


#Drop unnecessary columns as per EDA inferences
df = data.drop(columns=['Country', 'Date', 'Year', 'Day'])


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Local                     418 non-null    object
 1   Industry Sector           418 non-null    object
 2   Accident Level            418 non-null    object
 3   Potential Accident Level  418 non-null    object
 4   Gender                    418 non-null    object
 5   EmployeeType              418 non-null    object
 6   Critical Risk             418 non-null    object
 7   Description               418 non-null    object
 8   Month                     418 non-null    int64 
 9   WeekDay                   418 non-null    object
 10  Season                    418 non-null    object
dtypes: int64(1), object(10)
memory usage: 36.0+ KB


# to use regular expressions for manipulating text data
import re

# to load the natural language toolkit
import nltk
nltk.download('stopwords')    # loading the stopwords
nltk.download('wordnet')    # loading the wordnet module that is used in stemming

# to remove common stop words
from nltk.corpus import stopwords

# to perform stemming
from nltk.stem.porter import PorterStemmer

# to create Bag of Words
from sklearn.feature_extraction.text import CountVectorizer

# to import Word2Vec
from gensim.models import Word2Vec

# to import Glove
from gensim.scripts.glove2word2vec import glove2word2vec

#to import TFID
from sklearn.feature_extraction.text import TfidfVectorizer

# to display full text in dataframe
pd.set_option('display.max_colwidth', None)

#to tokenize
import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize

# to Lemmetize
from nltk.stem import WordNetLemmatizer
nltk.download("omw-1.4")

[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...

True


#copying to dataframe for nlp processing
df_nlp=df.copy()


# defining a function to remove special characters
def remove_special_characters(text):
    # Defining the regex pattern to match non-alphanumeric characters
    pattern = '[^A-Za-z0-9]+'

    # Finding the specified pattern and replacing non-alphanumeric characters with a blank string
    new_text = ''.join(re.sub(pattern, ' ', text))

    return new_text


# Applying the function to remove special characters
df_nlp['cleaned_text'] = data['Description'].apply(remove_special_characters)


# Checking cleaned data
df_nlp.loc[0:2, ['Description','cleaned_text']]


# Changing test to lower case
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].str.lower()

# Checking lower case data
df_nlp.loc[0:2, ['Description','cleaned_text']]


# Removing extra whitespaces from the text
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].str.strip()

# Checking cleaned data
df_nlp.loc[0:2, ['Description','cleaned_text']]


# Removing extra whitespaces from the text
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].replace(to_replace=r'\d', value='', regex=True)

# Checking cleaned data
df_nlp.loc[0:2, ['Description','cleaned_text']]


# Defining a function to remove stop words (like pronouns) using the NLTK library
def remove_stopwords(text):
    # Split text into separate words
    words = text.split()

    # Removing English language stopwords
    new_text = ' '.join([word for word in words if word not in stopwords.words('english')])

    return new_text


# Applying the function to remove stop words using the NLTK library
df_nlp['cleaned_text_without_stopwords'] = df_nlp['cleaned_text'].apply(remove_stopwords)

# Checking cleaned data with no stopwords
df_nlp.loc[0:2,['cleaned_text','cleaned_text_without_stopwords']]


#Tokenize sentence into words
df_nlp['cleaned_text_stopwords_token'] = df_nlp['cleaned_text_without_stopwords'].apply(word_tokenize)

# Checking token data
df_nlp.loc[0:2,['cleaned_text_stopwords_token','cleaned_text_without_stopwords']]


# Initializing Lemmatizer
wnl = WordNetLemmatizer()

def apply_lemmetizer(text):

    for word in text:
      new_text = ' '.join([wnl.lemmatize(word) for word in text])

    return new_text


# Applying the function to perform lemmetization
df_nlp['final_cleaned_text'] = df_nlp['cleaned_text_stopwords_token'].apply(apply_lemmetizer)

# checking a couple of instances of cleaned data
df_nlp.loc[0:2,['cleaned_text_without_stopwords','final_cleaned_text']]


# Initializing CountVectorizer with top 1000 words
bow_vec = CountVectorizer(max_features = 1000)

# Applying TfidfVectorizer on data
data_features_BOW = bow_vec.fit_transform(df_nlp['final_cleaned_text'])

# Convert the data features to array
data_features_BOW = data_features_BOW.toarray()

# Shape of the feature vector
data_features_BOW.shape

(418, 1000)


# Getting the 1000 words considered by the BoW model
words = bow_vec.get_feature_names_out()
# Checking the words considered by BoW model
words

array(['able', 'abruptly', 'access', 'accessory', 'accident',
       'accompanied', 'accumulation', 'acid', 'across', 'action',
       'activated', 'activates', 'activity', 'additive', 'adjustment',
       'advance', 'affected', 'ahead', 'aid', 'air', 'albino', 'align',
       'allergic', 'allergy', 'alone', 'along', 'aluminum', 'amount',
       'ampoloader', 'anchor', 'anfoloader', 'ankle', 'anode', 'another',
       'apparently', 'approach', 'approaching', 'approx', 'approximate',
       'approximately', 'area', 'arm', 'around', 'arranged', 'asks',
       'assembly', 'assistant', 'assisted', 'atlas', 'attached', 'attack',
       'attacked', 'attempt', 'attended', 'attention', 'autoclave',
       'auxiliary', 'avoid', 'away', 'back', 'backwards', 'bag',
       'balance', 'bank', 'bar', 'barbed', 'base', 'basket', 'beam',
       'bearing', 'bee', 'begin', 'behind', 'belt', 'bend', 'big', 'bit',
       'bite', 'bitten', 'blade', 'blasting', 'block', 'blocked', 'blow',
       'blowing', 'blunt', 'board', 'body', 'boiler', 'bolt', 'boltec',
       'bolter', 'bomb', 'boot', 'bottom', 'bounce', 'box', 'bp', 'brace',
       'brake', 'branch', 'break', 'broken', 'bruise', 'bucket', 'bumped',
       'burn', 'burning', 'cabin', 'cabinet', 'cable', 'called', 'came',
       'camera', 'canvas', 'cap', 'car', 'care', 'carried', 'carry',
       'carrying', 'cart', 'cat', 'cathode', 'caught', 'cause', 'caused',
       'causing', 'cell', 'cement', 'center', 'central', 'cervical',
       'chain', 'chamber', 'change', 'channel', 'check', 'cheekbone',
       'chest', 'chimney', 'chin', 'chisel', 'chuck', 'chute', 'chuteo',
       'circumstance', 'city', 'clamp', 'clean', 'cleaned', 'cleaning',
       'clearing', 'clerk', 'climb', 'climbing', 'clinic', 'close',
       'closed', 'closing', 'cloth', 'cm', 'cmxcmxcm', 'co', 'cocada',
       'coil', 'collaborator', 'colleague', 'collection', 'come',
       'coming', 'communicates', 'company', 'complete', 'completed',
       'composed', 'compressed', 'concentrate', 'concrete', 'cone',
       'connection', 'contact', 'container', 'contaminated', 'continue',
       'continued', 'control', 'contusion', 'conveyor', 'convoy',
       'coordinated', 'cord', 'corner', 'corrugated', 'could', 'coupling',
       'cover', 'covered', 'crane', 'cross', 'crown', 'current', 'cut',
       'cutter', 'cutting', 'cx', 'cylinder', 'da', 'day', 'de', 'decide',
       'decided', 'decides', 'deep', 'degree', 'descending', 'described',
       'detached', 'diameter', 'dining', 'directed', 'direction',
       'disassembled', 'discharge', 'discomfort', 'disk', 'displacement',
       'distal', 'distance', 'divine', 'doctor', 'door', 'drainage',
       'drawer', 'drill', 'driller', 'drilling', 'driver', 'drop',
       'dropped', 'dry', 'due', 'dust', 'duty', 'ear', 'easel', 'edge',
       'effect', 'effort', 'electric', 'electrical', 'electrician',
       'electrolysis', 'electrowelded', 'embedded', 'emergency',
       'employee', 'empty', 'encountered', 'end', 'energized', 'engine',
       'engineer', 'enter', 'entered', 'entering', 'enters', 'entrance',
       'entry', 'epp', 'epps', 'equipment', 'evacuated', 'evacuation',
       'evaluate', 'evaluated', 'evaluation', 'event', 'exchange',
       'excoriation', 'execution', 'exit', 'explosive', 'extension',
       'eye', 'face', 'fact', 'fall', 'falling', 'false', 'fan', 'feeder',
       'feel', 'fell', 'felt', 'fence', 'fenced', 'field', 'fifth',
       'filling', 'filter', 'find', 'finding', 'fine', 'finger',
       'finished', 'fire', 'first', 'fit', 'fixed', 'fixing', 'flange',
       'flash', 'floor', 'flow', 'followed', 'following', 'foot', 'force',
       'forearm', 'forehead', 'forest', 'forklift', 'form', 'forward',
       'found', 'four', 'fourth', 'fracture', 'fragment', 'frame', 'free',
       'front', 'fuel', 'full', 'furnace', 'gable', 'gallery', 'gate',
       'general', 'generates', 'generating', 'geological', 'geologist',
       'get', 'getting', 'girdle', 'give', 'glass', 'glove', 'go',
       'goggles', 'going', 'got', 'gps', 'gr', 'grab', 'grating', 'grid',
       'ground', 'grp', 'guard', 'guide', 'guillotine', 'gun', 'gutter',
       'hammer', 'hand', 'handle', 'handling', 'happened', 'hat', 'hdp',
       'hdpe', 'head', 'heading', 'heard', 'heated', 'heavy', 'height',
       'held', 'helical', 'helmet', 'help', 'helper', 'high', 'highway',
       'hit', 'hitting', 'hoist', 'hold', 'holder', 'holding', 'hole',
       'hook', 'hooked', 'hopper', 'horizontally', 'horse', 'hose',
       'hospital', 'hot', 'hour', 'house', 'housing', 'hq', 'hurried',
       'hw', 'hydraulic', 'identified', 'identify', 'immediately',
       'impact', 'impacted', 'impacting', 'imprisoned', 'imprisons',
       'impromec', 'inch', 'incident', 'incimmet', 'including',
       'increase', 'index', 'indicated', 'industrial', 'ingot', 'initial',
       'injured', 'injury', 'inner', 'inside', 'inspect', 'inspection',
       'installation', 'installing', 'instant', 'instep', 'interior',
       'intermediate', 'internal', 'intersection', 'involved', 'iron',
       'irritation', 'jacket', 'jhonatan', 'jumbo', 'jump', 'key', 'kg',
       'km', 'knee', 'knife', 'la', 'ladder', 'lamp', 'lane', 'last',
       'later', 'leaching', 'leaf', 'leak', 'leakage', 'leaning',
       'leather', 'leave', 'leaving', 'left', 'leg', 'length', 'lens',
       'lesion', 'level', 'lever', 'license', 'lid', 'lift', 'lifted',
       'lifting', 'light', 'limb', 'line', 'lip', 'liquid', 'litorina',
       'little', 'load', 'loader', 'loading', 'local', 'localized',
       'located', 'lock', 'locked', 'locking', 'locomotive', 'long',
       'loose', 'loosen', 'lose', 'loses', 'loud', 'low', 'lower', 'lt',
       'luis', 'lunch', 'machete', 'machine', 'made', 'magnetometric',
       'maid', 'main', 'maintenance', 'make', 'making', 'maneuver',
       'manipulating', 'manoel', 'manual', 'manually', 'maperu',
       'mapping', 'marco', 'maribondos', 'mask', 'master', 'material',
       'mean', 'mechanic', 'mechanical', 'medical', 'member', 'mesh',
       'metal', 'metallic', 'metatarsal', 'meter', 'middle', 'mild',
       'mill', 'mine', 'mineral', 'mining', 'minor', 'mixkret', 'mobile',
       'moment', 'mooring', 'motor', 'mouth', 'move', 'moved', 'movement',
       'moving', 'mr', 'mt', 'mud', 'mx', 'nail', 'natclar', 'nd', 'near',
       'necessary', 'neck', 'need', 'needed', 'negative', 'next',
       'nilton', 'nio', 'noise', 'normal', 'normally', 'nose', 'note',
       'notice', 'noticed', 'noticing', 'nut', 'nv', 'ob', 'observes',
       'occurred', 'occurs', 'office', 'official', 'oil', 'old', 'one',
       'onto', 'op', 'open', 'opened', 'opening', 'operates', 'operation',
       'operator', 'order', 'ore', 'originating', 'orlando', 'outside',
       'oven', 'overflow', 'pad', 'pain', 'palm', 'paracatu', 'paralyzed',
       'parked', 'parking', 'part', 'particle', 'partner', 'pasco',
       'pass', 'passed', 'passing', 'patrol', 'paulo', 'people',
       'perforation', 'perform', 'performed', 'performing', 'performs',
       'person', 'personnel', 'phalanx', 'phase', 'pick', 'piece',
       'pierced', 'pilot', 'pin', 'pipe', 'pit', 'place', 'placed',
       'placing', 'plant', 'plate', 'platform', 'plug', 'pm', 'point',
       'pole', 'polyethylene', 'position', 'positioned', 'positioning',
       'positive', 'post', 'pot', 'pound', 'power', 'ppe', 'preparation',
       'preparing', 'presence', 'press', 'pressed', 'pressing',
       'pressure', 'prevent', 'prevented', 'previous', 'previously',
       'probe', 'problem', 'procedure', 'proceed', 'proceeded',
       'proceeding', 'proceeds', 'process', 'producing', 'product',
       'project', 'projected', 'projecting', 'projection', 'prospector',
       'protection', 'protective', 'protector', 'protruding', 'provoking',
       'pull', 'pulley', 'pulling', 'pulp', 'pump', 'pumping', 'push',
       'pushing', 'put', 'pvc', 'quickly', 'quirodactyl', 'radial',
       'radio', 'radius', 'rail', 'railing', 'raise', 'raised', 'ramp',
       'ran', 'rb', 'rd', 'reach', 'reached', 'reaching', 'reaction',
       'realized', 'realizing', 'rear', 'rebound', 'received',
       'receiving', 'reconnaissance', 'recovery', 'reduced', 'reducer',
       'referred', 'region', 'release', 'released', 'releasing',
       'remaining', 'removal', 'remove', 'removed', 'removing', 'report',
       'reported', 'residual', 'residue', 'respective', 'rest', 'resting',
       'resulting', 'return', 'returned', 'returning', 'rice', 'right',
       'rim', 'ring', 'ripper', 'road', 'robson', 'rock', 'rod', 'roll',
       'roof', 'room', 'rope', 'rotation', 'routine', 'row', 'rub',
       'rubber', 'rung', 'safe', 'safety', 'said', 'sample', 'sampling',
       'saw', 'scaffolding', 'scissor', 'scoop', 'scooptram', 'seat',
       'second', 'section', 'see', 'service', 'set', 'several', 'shank',
       'shape', 'shear', 'sheet', 'shifted', 'shock', 'shoe', 'short',
       'shotcrete', 'shoulder', 'side', 'silva', 'since', 'sink', 'site',
       'sleeve', 'slid', 'slide', 'sliding', 'slight', 'slightly',
       'sling', 'slip', 'slipped', 'slope', 'small', 'soil', 'sole',
       'solution', 'soon', 'sound', 'south', 'space', 'splash', 'split',
       'square', 'squat', 'st', 'staff', 'stage', 'staircase', 'standing',
       'start', 'started', 'starting', 'state', 'station', 'steam',
       'steel', 'step', 'stepped', 'still', 'stilson', 'sting', 'stone',
       'stool', 'stop', 'stopped', 'store', 'storm', 'strap', 'strike',
       'striking', 'strip', 'struck', 'structure', 'strut', 'stuck',
       'stumble', 'stump', 'stung', 'suction', 'suddenly', 'suffered',
       'suffering', 'suffers', 'sulfuric', 'superficial', 'supervising',
       'supervision', 'supervisor', 'supply', 'support', 'supported',
       'supporting', 'surface', 'surprised', 'survey', 'suspended',
       'sustained', 'swelling', 'system', 'table', 'take', 'taken',
       'tank', 'target', 'task', 'team', 'technician', 'telescopic',
       'tell', 'tension', 'test', 'th', 'thermal', 'thickener', 'third',
       'thorn', 'three', 'throw', 'throwing', 'thumb', 'tie', 'tilted',
       'time', 'tip', 'tipper', 'tire', 'together', 'ton', 'took', 'tool',
       'top', 'toward', 'towards', 'tower', 'track', 'transfer',
       'transferred', 'transmission', 'transport', 'transporting',
       'trapping', 'traveled', 'traveling', 'treated', 'treatment',
       'tried', 'trip', 'truck', 'try', 'trying', 'tube', 'tubing',
       'turn', 'turned', 'turning', 'twist', 'twisting', 'two', 'type',
       'unbalanced', 'unclog', 'unexpectedly', 'unicon', 'uniform',
       'unit', 'unloading', 'unlocking', 'untimely', 'upon', 'upper',
       'us', 'use', 'used', 'using', 'ustulado', 'ustulation', 'valve',
       'van', 'vegetation', 'vehicle', 'ventilation', 'verified',
       'verify', 'vertical', 'victim', 'wagon', 'walk', 'walking', 'wall',
       'warehouse', 'washing', 'wasp', 'waste', 'water', 'way', 'wearing',
       'weight', 'welder', 'welding', 'well', 'went', 'wheel', 'wick',
       'william', 'winch', 'window', 'wire', 'withdrawal', 'without',
       'wood', 'wooden', 'wore', 'work', 'worker', 'working', 'workshop',
       'would', 'wound', 'wrench', 'wrist', 'xxcm', 'zinc', 'zone'],
      dtype=object)


# Creating a DataFrame from the data features
df_BOW = pd.DataFrame(data_features_BOW, columns=bow_vec.get_feature_names_out())
df_BOW.head()


# creating a list of all words in our data
words_list = [item.split(" ") for item in df_nlp['final_cleaned_text'].values]

# Checking the words from the first five reviews
words_list[0:5]

[['removing',
  'drill',
  'rod',
  'jumbo',
  'maintenance',
  'supervisor',
  'proceeds',
  'loosen',
  'support',
  'intermediate',
  'centralizer',
  'facilitate',
  'removal',
  'seeing',
  'mechanic',
  'support',
  'one',
  'end',
  'drill',
  'equipment',
  'pull',
  'hand',
  'bar',
  'accelerate',
  'removal',
  'moment',
  'bar',
  'slide',
  'point',
  'support',
  'tightens',
  'finger',
  'mechanic',
  'drilling',
  'bar',
  'beam',
  'jumbo'],
 ['activation',
  'sodium',
  'sulphide',
  'pump',
  'piping',
  'uncoupled',
  'sulfide',
  'solution',
  'designed',
  'area',
  'reach',
  'maid',
  'immediately',
  'made',
  'use',
  'emergency',
  'shower',
  'directed',
  'ambulatory',
  'doctor',
  'later',
  'hospital',
  'note',
  'sulphide',
  'solution',
  'gram',
  'liter'],
 ['sub',
  'station',
  'milpo',
  'located',
  'level',
  'collaborator',
  'excavation',
  'work',
  'pick',
  'hand',
  'tool',
  'hitting',
  'rock',
  'flat',
  'part',
  'beak',
  'bounce',
  'hitting',
  'steel',
  'tip',
  'safety',
  'shoe',
  'metatarsal',
  'area',
  'left',
  'foot',
  'collaborator',
  'causing',
  'injury'],
 ['approximately',
  'nv',
  'cx',
  'ob',
  'personnel',
  'begin',
  'task',
  'unlocking',
  'soquet',
  'bolt',
  'bhb',
  'machine',
  'penultimate',
  'bolt',
  'identified',
  'hexagonal',
  'head',
  'worn',
  'proceeding',
  'mr',
  'crist',
  'bal',
  'auxiliary',
  'assistant',
  'climb',
  'platform',
  'exert',
  'pressure',
  'hand',
  'dado',
  'key',
  'prevent',
  'coming',
  'bolt',
  'moment',
  'two',
  'collaborator',
  'rotate',
  'lever',
  'anti',
  'clockwise',
  'direction',
  'leaving',
  'key',
  'bolt',
  'hitting',
  'palm',
  'left',
  'hand',
  'causing',
  'injury'],
 ['approximately',
  'circumstance',
  'mechanic',
  'anthony',
  'group',
  'leader',
  'eduardo',
  'eric',
  'fern',
  'ndez',
  'injured',
  'three',
  'company',
  'impromec',
  'performed',
  'removal',
  'pulley',
  'motor',
  'pump',
  'zaf',
  'marcy',
  'cm',
  'length',
  'cm',
  'weight',
  'kg',
  'locked',
  'proceed',
  'heating',
  'pulley',
  'loosen',
  'come',
  'fall',
  'distance',
  'meter',
  'high',
  'hit',
  'instep',
  'right',
  'foot',
  'worker',
  'causing',
  'injury',
  'described']]


# creating an instance of Word2Vec
model_W2V = Word2Vec(words_list, vector_size = 100, min_count = 1, window=5, workers = 6)
# Checking the size of the vocabulary
print("Length of the vocabulary is", len(list(model_W2V.wv.key_to_index)))

Length of the vocabulary is 2800


# Dictionary with key as words and the value as the embedding vector.

words = list(model_W2V.wv.key_to_index.keys())

# Retrieving word vectors for all the words present in the model's vocabulary
wvs = model_W2V.wv[words].tolist()

# Creating a dictionary of words and their corresponding vectors
word_vector_dict = dict(zip(words, wvs))


def average_vectorizer_Word2Vec(doc):
    # Initializing a feature vector for the sentence
    feature_vector = np.zeros((vec_size,), dtype="float64")

    # Creating a list of words in the sentence that are present in the model vocabulary
    words_in_vocab = [word for word in doc.split() if word in words]

    # adding the vector representations of the words
    for word in words_in_vocab:
        feature_vector += np.array(word_vector_dict[word])

    # Dividing by the number of words to get the average vector
    if len(words_in_vocab) != 0:
        feature_vector /= len(words_in_vocab)

    return feature_vector


# creating a dataframe of the vectorized documents
vec_size = 100
df_word2vec = pd.DataFrame(df_nlp['final_cleaned_text'].apply(average_vectorizer_Word2Vec).tolist(), columns=['Feature '+str(i) for i in range(vec_size)])
df_word2vec


pip install gensim

Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.3)
Requirement already satisfied: numpy<2.0,>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.26.4)
Requirement already satisfied: scipy<1.14.0,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.13.1)
Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (7.0.4)
Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open>=1.8.1->gensim) (1.16.0)


#use this if you are directly downloading from the site

#!wget http://nlp.stanford.edu/data/glove.6B.zip
#!unzip glove.6B.zip

#from gensim.scripts.glove2word2vec import glove2word2vec
#glove_input_file = 'glove.6B.100d.txt'
#word2vec_output_file = 'glove.6B.100d.txt.word2vec'
#glove2word2vec(glove_input_file, word2vec_output_file)


from gensim.scripts.glove2word2vec import glove2word2vec
glove_input_file = '/content/drive/My Drive/Colab Notebooks/glove.6B.100d.txt'
word2vec_output_file = 'glove.6B.100d.txt.word2vec'
glove2word2vec(glove_input_file, word2vec_output_file)

(400000, 100)


from gensim.models import KeyedVectors
# load the Stanford GloVe model
filename = 'glove.6B.100d.txt.word2vec'
glove_model = KeyedVectors.load_word2vec_format(filename, binary=False)


# Checking the size of the vocabulary
print("Length of the vocabulary is", len(glove_model.index_to_key))

Length of the vocabulary is 400000


#List of words in the vocabulary
words = glove_model.index_to_key
#Dictionary with key as the word and the value as the corresponding embedding vector.
word_vector_dict = dict(zip(glove_model.index_to_key,list(glove_model.vectors)))
#Defining the dimension of the embedded vector.
vec_size=100


def average_vectorizer_GloVe(doc):
    # Initializing a feature vector for the sentence
    feature_vector = np.zeros((vec_size,), dtype="float64")

    # Creating a list of words in the sentence that are present in the model vocabulary
    words_in_vocab = [word for word in doc.split() if word in words]

    # adding the vector representations of the words
    for word in words_in_vocab:
        feature_vector += np.array(word_vector_dict[word])

    # Dividing by the number of words to get the average vector
    if len(words_in_vocab) != 0:
        feature_vector /= len(words_in_vocab)

    return feature_vector


# Creating a dataframe of the vectorized documents
df_glove = pd.DataFrame(df_nlp['final_cleaned_text'].apply(average_vectorizer_GloVe).tolist(), columns=['Feature '+str(i) for i in range(vec_size)])
df_glove


vec_tfidf = TfidfVectorizer()

corpus=df_nlp['final_cleaned_text'].values
# Fit and transform the corpus
tfidf = vec_tfidf.fit_transform(corpus)

# Get the feature names (words)
features = vec_tfidf.get_feature_names_out()

# Print the feature names
print("Feature names:", features)

# Convert TF-IDF matrix to DataFrame
df_tfidf = pd.DataFrame(tfidf.toarray(), columns=features)
df_tfidf

Feature names: ['abb' 'abdomen' 'able' ... 'zinco' 'zn' 'zone']


df_nlp.head(2)


#deleting unwanted columns
df_nlp_cln=df_nlp.drop(['Description', 'cleaned_text', 'cleaned_text_without_stopwords', 'cleaned_text_stopwords_token'], axis=1)
df_nlp_cln.info()


df_nlp_cln.head(2)


#Since Weekday, Seasons, Accident level and Potential Accident level are ordinal, we will apply Label Encoding on these two columns
le = LabelEncoder()
df_nlp_cln['Accident Level'] = le.fit_transform(df_nlp_cln['Accident Level']).astype(np.int8)
df_nlp_cln['Potential Accident Level'] = le.fit_transform(df_nlp_cln['Potential Accident Level']).astype(np.int8)
df_nlp_cln['Season'] = le.fit_transform(df_nlp_cln['Season']).astype(np.int8)
df_nlp_cln['WeekDay'] = le.fit_transform(df_nlp_cln['WeekDay']).astype(np.int8)

df_nlp_cln.head(5)


#Applying One Hot Encoding using dummy values approach on other columns
Local_dummies = pd.get_dummies(df_nlp_cln['Local'], columns=["Local"], drop_first=True).astype(np.int8)
Gender_dummies = pd.get_dummies(df_nlp_cln['Gender'], columns=["Gender"], drop_first=True).astype(np.int8)
Industry_dummies = pd.get_dummies(df_nlp_cln['Industry Sector'], columns=['Industry Sector'], prefix='IS', drop_first=True).astype(np.int8)
EmpType_dummies = pd.get_dummies(df_nlp_cln['EmployeeType'], columns=['EmployeeType'], prefix='EmpType', drop_first=True).astype(np.int8)
CR_dummies = pd.get_dummies(df_nlp_cln['Critical Risk'], columns=['Critical Risk'], prefix='CR', drop_first=True).astype(np.int8)


#Merge the above dataframe with the original dataframe
df_nlp_cln= df_nlp_cln.join(Local_dummies.reset_index(drop=True)).join(Gender_dummies.reset_index(drop=True)).join(Industry_dummies.reset_index(drop=True)).join(EmpType_dummies.reset_index(drop=True)).join(CR_dummies.reset_index(drop=True))
df_nlp_cln.head()


df_nlp_cln.info()


#deleting original columns that were one hot encoded
df_nlp_cln.drop(['Local', 'Gender', 'Industry Sector', 'EmployeeType', 'Critical Risk','final_cleaned_text'], axis=1, inplace=True)
df_nlp_cln.head(1)


#Merging the data with Glove dataframe
feature_df_final_glove = df_nlp_cln.join(pd.DataFrame(df_glove).reset_index(drop=True))
feature_df_final_glove.head()


#Saving the data to an excel sheet
feature_df_final_glove.to_excel('drive/My Drive/AIML/feature_df_final_glove.xlsx')


#Merging the data with word2vec dataframe
feature_df_final_word2vec = df_nlp_cln.join(pd.DataFrame(df_word2vec).reset_index(drop=True))
feature_df_final_word2vec.head()


#Saving the data to an excel sheet
feature_df_final_word2vec.to_excel('drive/My Drive/AIML/feature_df_final_word2vec.xlsx')


#Merging the data with TFIDF dataframe
feature_df_final_tfidf = df_nlp_cln.join(pd.DataFrame(df_tfidf).reset_index(drop=True))
feature_df_final_tfidf.head()


#Saving the data to an excel sheet
feature_df_final_tfidf.to_excel('drive/My Drive/AIML/feature_df_final_tfidf.xlsx')


# splitting data into X and y- skip this step of you are importing data from the saved feature set file
"""
X_w2v = feature_df_final_word2vec.drop(['Accident Level','Potential Accident Level'], axis = 1)
X_glove = feature_df_final_glove.drop(['Accident Level','Potential Accident Level'], axis = 1)
X_tfidf = feature_df_final_tfidf.drop(['Accident Level','Potential Accident Level'], axis = 1)
y_w2v = feature_df_final_word2vec['Accident Level']
y_glove= feature_df_final_glove['Accident Level']
y_tfidf= feature_df_final_tfidf['Accident Level']

#for potential accident level as target
y_w2v_pot = feature_df_final_word2vec['Potential Accident Level']
y_glove_pot= feature_df_final_glove['Potential Accident Level']
y_tfidf_pot= feature_df_final_tfidf['Potential Accident Level']"""


#df_w2v_import = pd.read_excel('drive/My Drive/AIML/feature_df_final_word2vec.xlsx')
#df_glove_import = pd.read_excel('drive/My Drive/AIML/feature_df_final_glove.xlsx')
#df_tfidf_import= pd.read_excel('drive/My Drive/AIML/feature_df_final_tfidf.xlsx')
df_w2v_import = pd.read_excel('drive/My Drive/AI ML - UT Austin/Project/feature_df_final_word2vec.xlsx')
df_glove_import = pd.read_excel('drive/My Drive/AI ML - UT Austin/Project/feature_df_final_glove.xlsx')
df_tfidf_import= pd.read_excel('drive/My Drive/AI ML - UT Austin/Project/feature_df_final_tfidf.xlsx')


#Alternate Read for Files from Local PC
df_w2v_import = pd.read_excel('feature_df_final_word2vec.xlsx')
df_glove_import = pd.read_excel('feature_df_final_glove.xlsx')
df_tfidf_import= pd.read_excel('feature_df_final_tfidf.xlsx')


df_w2v_import.head(2)


#Deleting Unnamed column
df_w2v_import = df_w2v_import.drop(df_w2v_import.columns[[0]],axis = 1)
df_w2v_import.head(2)


#Deleting Unnamed column
df_glove_import = df_glove_import.drop(df_glove_import.columns[[0]],axis = 1)
df_tfidf_import = df_tfidf_import.drop(df_tfidf_import.columns[[0]],axis = 1)


X_w2v = df_w2v_import.drop(['Accident Level','Potential Accident Level'], axis = 1)
X_glove = df_glove_import.drop(['Accident Level','Potential Accident Level'], axis = 1)
X_tfidf = df_tfidf_import.drop(['Accident Level','Potential Accident Level'], axis = 1)
y_w2v = df_w2v_import['Accident Level']
y_glove= df_glove_import['Accident Level']
y_tfidf= df_tfidf_import['Accident Level']

#for potential accident level as target
y_w2v_pot = df_w2v_import['Potential Accident Level']
y_glove_pot= df_glove_import['Potential Accident Level']
y_tfidf_pot= df_tfidf_import['Potential Accident Level']


def split(X,y):
    # Initial split into training (80%) and testing (20%)
    if np.min(np.bincount(y)) < 2:
    #Remove stratification if y has single sample - required in case of potential accident level V
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
    else:
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, stratify=y, random_state=42)
    return X_train,X_test,y_train,y_test


#Splitting the dataset
X_train_w2v,X_test_w2v,y_train_w2v,y_test_w2v=split(X_w2v,y_w2v)
X_train_glove,X_test_glove,y_train_glove,y_test_glove=split(X_glove,y_glove)
X_train_tfidf,X_test_tfidf,y_train_tfidf,y_test_tfidf=split(X_tfidf,y_tfidf)


print(X_train_w2v.shape, X_test_w2v.shape, y_train_w2v.shape, y_test_w2v.shape)
print(X_train_glove.shape, X_test_glove.shape, y_train_glove.shape, y_test_glove.shape)
print(X_train_tfidf.shape, X_test_tfidf.shape, y_train_tfidf.shape, y_test_tfidf.shape)

(334, 151) (84, 151) (334,) (84,)
(334, 151) (84, 151) (334,) (84,)
(334, 2838) (84, 2838) (334,) (84,)


#Define Standardization of data
def standard_scaler(X_train, X_test):
    scaler = StandardScaler().set_output(transform="pandas")
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)
    return X_train_scaled, X_test_scaled


# Define PCA dimensiom reduction
def pca_sklearn(X_train, X_test, n_components):
    pca = PCA(n_components=n_components).set_output(transform="pandas")
    X_train_pca = pca.fit_transform(X_train)
    X_test_pca = pca.transform(X_test)
    return X_train_pca, X_test_pca


# Data balancing- Upsampling

def oversampling(X_train, y_train, target):
    X_upsample = pd.concat([X_train, y_train], axis=1)
    # Get the majority and minority class
    acclevel_0 = X_upsample[X_upsample['Accident Level'] == 0]
    acclevel_1 = X_upsample[X_upsample['Accident Level'] == 1]
    acclevel_2 = X_upsample[X_upsample['Accident Level'] == 2]
    acclevel_3 = X_upsample[X_upsample['Accident Level'] == 3]
    acclevel_4 = X_upsample[X_upsample['Accident Level'] == 4]

# Upsample minority classes
    acclevel1_upsampled = resample(acclevel_1, replace = True, n_samples = len(acclevel_0), random_state = 1)
    acclevel2_upsampled = resample(acclevel_2, replace = True, n_samples = len(acclevel_0), random_state = 1)
    acclevel3_upsampled = resample(acclevel_3, replace = True, n_samples = len(acclevel_0), random_state = 1)
    acclevel4_upsampled = resample(acclevel_4, replace = True, n_samples = len(acclevel_0), random_state = 1)
    df_upsampled = pd.concat([acclevel_0, acclevel1_upsampled, acclevel2_upsampled, acclevel3_upsampled, acclevel4_upsampled])
    print(df_upsampled[target].value_counts())
    #df_upsampled.to_csv(file_name)
    X_train_up = df_upsampled.drop([target], axis = 1) # Considering all Predictors
    y_train_up = df_upsampled[target]
    return X_train_up, y_train_up


# Data balancing- SMOTE
def SMOTE_upsampling(X_train, y_train):
    sm = SMOTE(random_state=7)
    X_train_smote, y_train_smote = sm.fit_resample(X_train, y_train)
    print(y_train_smote.value_counts())
    return X_train_smote, y_train_smote


# PCA and Upsampled
X_train_w2v_pca, X_test_w2v_pca = pca_sklearn(X_train_w2v, X_test_w2v, 0.95)
X_train_w2v_up, y_train_w2v_up = oversampling(X_train_w2v_pca, y_train_w2v, 'Accident Level')

# X_train_w2v_up, y_train_w2v_up, X_test_w2v_pca, y_test_w2v - use these to pass on  to model

Accident Level
0    247
1    247
2    247
3    247
4    247
Name: count, dtype: int64


# PCA and SMOTE
X_train_w2v_pca, X_test_w2v_pca = pca_sklearn(X_train_w2v, X_test_w2v, 0.95)
X_train_w2v_smote, y_train_w2v_smote = SMOTE_upsampling(X_train_w2v_pca, y_train_w2v, 'Accident Level')

# X_train_w2v_smote, y_train_w2v_smote, X_test_w2v_pca, y_test_w2v - use these to pass on  to model

Accident Level
0    247
1    247
2    247
3    247
4    247
Name: count, dtype: int64


# PCA and Upsampled
X_train_glove_pca, X_test_glove_pca = pca_sklearn(X_train_glove, X_test_glove, 0.95)
X_train_glove_up, y_train_glove_up = oversampling(X_train_glove_pca, y_train_glove, 'Accident Level')

# X_train_glove_up, y_train_glove_up, X_test_glove_pca, y_test_glove - use these to pass on  to model

Accident Level
0    247
1    247
2    247
3    247
4    247
Name: count, dtype: int64


# PCA and SMOTE
X_train_glove_pca, X_test_glove_pca = pca_sklearn(X_train_glove, X_test_glove, 0.95)
X_train_glove_smote, y_train_glove_smote = SMOTE_upsampling(X_train_glove_pca, y_train_glove, 'Accident Level')

# X_train_glove_smote, y_train_glove_smote, X_test_glove_pca, y_test_glove - use these to pass on  to model

Accident Level
0    247
1    247
2    247
3    247
4    247
Name: count, dtype: int64


# PCA and Upsampled
X_train_tfidf_pca, X_test_tfidf_pca = pca_sklearn(X_train_tfidf, X_test_tfidf, 0.95)
X_train_tfidf_up, y_train_tfidf_up = oversampling(X_train_tfidf_pca, y_train_tfidf, 'Accident Level')

# X_train_glove_up, y_train_glove_up, X_test_glove_pca, y_test_glove - use these to pass on  to model

Accident Level
0    247
1    247
2    247
3    247
4    247
Name: count, dtype: int64


# PCA and SMOTE
X_train_tfidf_pca, X_test_tfidf_pca = pca_sklearn(X_train_tfidf, X_test_tfidf, 0.95)
X_train_tfidf_smote, y_train_tfidf_smote = SMOTE_upsampling(X_train_tfidf_pca, y_train_tfidf, 'Accident Level')

# X_train_glove_smote, y_train_glove_smote, X_test_glove_pca, y_test_glove - use these to pass on  to model

Accident Level
0    247
1    247
2    247
3    247
4    247
Name: count, dtype: int64


def runAllModelDefault(X_train,Y_train,X_test,Y_test,descriptionOfData):
    listOfModel = {
        'KNN' : KNeighborsClassifier(n_neighbors=7,weights='distance'),
        'Ridge': RidgeClassifier(alpha=1.0,max_iter=1000,solver='auto',tol=1e-3),
        'LogisticRegression': LogisticRegression(max_iter=1000, random_state=7),
        'GradientBoost': GradientBoostingClassifier(random_state=7),
        'SVM': SVC(kernel= 'rbf'),
        'Bagging': BaggingClassifier(),
        'RandomForest': RandomForestClassifier(n_estimators = 100, max_depth = 7, random_state = 7, class_weight = "balanced"),
        'DecisionTree': DecisionTreeClassifier(criterion='entropy',random_state=7, max_depth=6, min_samples_leaf=5),
        'AdaBoost': AdaBoostClassifier(n_estimators= 50),
        'XGBoost': XGBClassifier(),
        #'Gradient Boost': GradientBoostingClassifier(n_estimators = 50, learning_rate = 0.05)
    }

    nameofModel=[]
    trainAccScore=[]
    testAccScore=[]
    precissionSc=[]
    recallSc=[]
    f1Sc=[]

    for name, model in listOfModel.items():
       print("="*100)
       print("Model : ",name)
       print("="*100)
       print("\n\n")
       model.fit(X_train,Y_train)
       y_predict = model.predict(X_test)
       trainAccuracyScore = model.score(X_train,Y_train)
       testAccuracyScore = model.score(X_test,Y_test)
       precisionScore = precision_score(Y_test,y_predict,average='weighted')
       recallScore = recall_score(Y_test,y_predict,average='weighted')
       f1Score = f1_score(Y_test,y_predict,average='weighted')
       print("=="*20)
       print('Classification - Heatmap')
       print("=="*20)
       print("\n")
       classificationMatrix = confusion_matrix(Y_test,y_predict)
       sns.heatmap(classificationMatrix,annot=True,fmt=".4f",xticklabels = ["I", "II", "III", "IV", "V"] , yticklabels = ["I", "II", "III", "IV", "V"] )
       plt.ylabel('Actual')
       plt.xlabel('Predicted')
       plt.show()
       print("\n")
       print("=="*20)
       classificationReport = classification_report(Y_test,y_predict)
       print('Classification - Report')
       print("=="*20)
       print("\n")
       print(classification_report)
       nameofModel.append(name)
       trainAccScore.append(trainAccuracyScore)
       testAccScore.append(testAccuracyScore)
       precissionSc.append(precisionScore)
       recallSc.append(recallScore)
       f1Sc.append(f1Score)

       resultDF = pd.DataFrame({'Description Of Data':descriptionOfData,
                                'ModelName':nameofModel,
                                'Train Accuracy':trainAccScore,
                                'Test Accuracy':testAccScore,
                                'Precision':precissionSc,
                                'Recall':recallSc,
                                'F1':f1Sc})
       print("=="*20)
       print("Models Performance Summary")
       print("=="*20)
       print(resultDF)
    return resultDF


combinedResultDF = pd.DataFrame(columns=['Description Of Data','ModelName','Train Accuracy','Test Accuracy','Precision','Recall','F1'])


#Basic Train and Test on each of medium of data - word2vec / glove / tfidf

combinedResultDF = pd.concat([combinedResultDF, runAllModelDefault(X_train=X_train_w2v,Y_train=y_train_w2v,X_test=X_test_w2v,Y_test=y_test_w2v,descriptionOfData="Word2Vec-Basic-Accident")], ignore_index=True,sort=False)
combinedResultDF = pd.concat([combinedResultDF, runAllModelDefault(X_train=X_train_glove,Y_train=y_train_glove,X_test=X_test_glove,Y_test=y_test_glove,descriptionOfData="Glove-Basic-Accident")], ignore_index=True,sort=False)
combinedResultDF = pd.concat([combinedResultDF, runAllModelDefault(X_train=X_train_tfidf,Y_train=y_train_tfidf,X_test=X_test_tfidf,Y_test=y_test_tfidf,descriptionOfData="TfIdf-Basic-Accident")], ignore_index=True,sort=False)

====================================================================================================
Model :  KNN
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
       Description Of Data ModelName  Train Accuracy  Test Accuracy  \
0  Word2Vec-Basic-Accident       KNN        0.997006       0.690476   

   Precision    Recall        F1  
0   0.555968  0.690476  0.615964  
====================================================================================================
Model :  Ridge
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
       Description Of Data ModelName  Train Accuracy  Test Accuracy  \
0  Word2Vec-Basic-Accident       KNN        0.997006       0.690476   
1  Word2Vec-Basic-Accident     Ridge        0.763473       0.738095   

   Precision    Recall        F1  
0   0.555968  0.690476  0.615964  
1   0.544785  0.738095  0.626875  
====================================================================================================
Model :  LogisticRegression
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
       Description Of Data           ModelName  Train Accuracy  Test Accuracy  \
0  Word2Vec-Basic-Accident                 KNN        0.997006       0.690476   
1  Word2Vec-Basic-Accident               Ridge        0.763473       0.738095   
2  Word2Vec-Basic-Accident  LogisticRegression        0.742515       0.726190   

   Precision    Recall        F1  
0   0.555968  0.690476  0.615964  
1   0.544785  0.738095  0.626875  
2   0.542456  0.726190  0.621018  
====================================================================================================
Model :  GradientBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
       Description Of Data           ModelName  Train Accuracy  Test Accuracy  \
0  Word2Vec-Basic-Accident                 KNN        0.997006       0.690476   
1  Word2Vec-Basic-Accident               Ridge        0.763473       0.738095   
2  Word2Vec-Basic-Accident  LogisticRegression        0.742515       0.726190   
3  Word2Vec-Basic-Accident       GradientBoost        0.997006       0.726190   

   Precision    Recall        F1  
0   0.555968  0.690476  0.615964  
1   0.544785  0.738095  0.626875  
2   0.542456  0.726190  0.621018  
3   0.549071  0.726190  0.625331  
====================================================================================================
Model :  SVM
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
       Description Of Data           ModelName  Train Accuracy  Test Accuracy  \
0  Word2Vec-Basic-Accident                 KNN        0.997006       0.690476   
1  Word2Vec-Basic-Accident               Ridge        0.763473       0.738095   
2  Word2Vec-Basic-Accident  LogisticRegression        0.742515       0.726190   
3  Word2Vec-Basic-Accident       GradientBoost        0.997006       0.726190   
4  Word2Vec-Basic-Accident                 SVM        0.739521       0.738095   

   Precision    Recall        F1  
0   0.555968  0.690476  0.615964  
1   0.544785  0.738095  0.626875  
2   0.542456  0.726190  0.621018  
3   0.549071  0.726190  0.625331  
4   0.544785  0.738095  0.626875  
====================================================================================================
Model :  Bagging
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
       Description Of Data           ModelName  Train Accuracy  Test Accuracy  \
0  Word2Vec-Basic-Accident                 KNN        0.997006       0.690476   
1  Word2Vec-Basic-Accident               Ridge        0.763473       0.738095   
2  Word2Vec-Basic-Accident  LogisticRegression        0.742515       0.726190   
3  Word2Vec-Basic-Accident       GradientBoost        0.997006       0.726190   
4  Word2Vec-Basic-Accident                 SVM        0.739521       0.738095   
5  Word2Vec-Basic-Accident             Bagging        0.967066       0.726190   

   Precision    Recall        F1  
0   0.555968  0.690476  0.615964  
1   0.544785  0.738095  0.626875  
2   0.542456  0.726190  0.621018  
3   0.549071  0.726190  0.625331  
4   0.544785  0.738095  0.626875  
5   0.549071  0.726190  0.625331  
====================================================================================================
Model :  RandomForest
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
       Description Of Data           ModelName  Train Accuracy  Test Accuracy  \
0  Word2Vec-Basic-Accident                 KNN        0.997006       0.690476   
1  Word2Vec-Basic-Accident               Ridge        0.763473       0.738095   
2  Word2Vec-Basic-Accident  LogisticRegression        0.742515       0.726190   
3  Word2Vec-Basic-Accident       GradientBoost        0.997006       0.726190   
4  Word2Vec-Basic-Accident                 SVM        0.739521       0.738095   
5  Word2Vec-Basic-Accident             Bagging        0.967066       0.726190   
6  Word2Vec-Basic-Accident        RandomForest        0.997006       0.738095   

   Precision    Recall        F1  
0   0.555968  0.690476  0.615964  
1   0.544785  0.738095  0.626875  
2   0.542456  0.726190  0.621018  
3   0.549071  0.726190  0.625331  
4   0.544785  0.738095  0.626875  
5   0.549071  0.726190  0.625331  
6   0.551348  0.738095  0.631199  
====================================================================================================
Model :  DecisionTree
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
       Description Of Data           ModelName  Train Accuracy  Test Accuracy  \
0  Word2Vec-Basic-Accident                 KNN        0.997006       0.690476   
1  Word2Vec-Basic-Accident               Ridge        0.763473       0.738095   
2  Word2Vec-Basic-Accident  LogisticRegression        0.742515       0.726190   
3  Word2Vec-Basic-Accident       GradientBoost        0.997006       0.726190   
4  Word2Vec-Basic-Accident                 SVM        0.739521       0.738095   
5  Word2Vec-Basic-Accident             Bagging        0.967066       0.726190   
6  Word2Vec-Basic-Accident        RandomForest        0.997006       0.738095   
7  Word2Vec-Basic-Accident        DecisionTree        0.799401       0.642857   

   Precision    Recall        F1  
0   0.555968  0.690476  0.615964  
1   0.544785  0.738095  0.626875  
2   0.542456  0.726190  0.621018  
3   0.549071  0.726190  0.625331  
4   0.544785  0.738095  0.626875  
5   0.549071  0.726190  0.625331  
6   0.551348  0.738095  0.631199  
7   0.559631  0.642857  0.598281  
====================================================================================================
Model :  AdaBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
       Description Of Data           ModelName  Train Accuracy  Test Accuracy  \
0  Word2Vec-Basic-Accident                 KNN        0.997006       0.690476   
1  Word2Vec-Basic-Accident               Ridge        0.763473       0.738095   
2  Word2Vec-Basic-Accident  LogisticRegression        0.742515       0.726190   
3  Word2Vec-Basic-Accident       GradientBoost        0.997006       0.726190   
4  Word2Vec-Basic-Accident                 SVM        0.739521       0.738095   
5  Word2Vec-Basic-Accident             Bagging        0.967066       0.726190   
6  Word2Vec-Basic-Accident        RandomForest        0.997006       0.738095   
7  Word2Vec-Basic-Accident        DecisionTree        0.799401       0.642857   
8  Word2Vec-Basic-Accident            AdaBoost        0.733533       0.726190   

   Precision    Recall        F1  
0   0.555968  0.690476  0.615964  
1   0.544785  0.738095  0.626875  
2   0.542456  0.726190  0.621018  
3   0.549071  0.726190  0.625331  
4   0.544785  0.738095  0.626875  
5   0.549071  0.726190  0.625331  
6   0.551348  0.738095  0.631199  
7   0.559631  0.642857  0.598281  
8   0.549071  0.726190  0.625331  
====================================================================================================
Model :  XGBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


pd.set_option('display.width',1000)
print(combinedResultDF)

        Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0   Word2Vec-Basic-Accident                 KNN        0.997006       0.690476   0.555968  0.690476  0.615964
1   Word2Vec-Basic-Accident               Ridge        0.763473       0.738095   0.544785  0.738095  0.626875
2   Word2Vec-Basic-Accident  LogisticRegression        0.742515       0.726190   0.542456  0.726190  0.621018
3   Word2Vec-Basic-Accident       GradientBoost        0.997006       0.726190   0.549071  0.726190  0.625331
4   Word2Vec-Basic-Accident                 SVM        0.739521       0.738095   0.544785  0.738095  0.626875
5   Word2Vec-Basic-Accident             Bagging        0.967066       0.726190   0.549071  0.726190  0.625331
6   Word2Vec-Basic-Accident        RandomForest        0.997006       0.738095   0.551348  0.738095  0.631199
7   Word2Vec-Basic-Accident        DecisionTree        0.799401       0.642857   0.559631  0.642857  0.598281
8   Word2Vec-Basic-Accident            AdaBoost        0.733533       0.726190   0.549071  0.726190  0.625331
9   Word2Vec-Basic-Accident             XGBoost        0.997006       0.714286   0.546737  0.714286  0.619381
10  Word2Vec-Basic-Accident      Gradient Boost        0.925150       0.726190   0.549071  0.726190  0.625331
11     Glove-Basic-Accident                 KNN        0.997006       0.726190   0.589286  0.726190  0.641600
12     Glove-Basic-Accident               Ridge        0.826347       0.702381   0.613321  0.702381  0.627635
13     Glove-Basic-Accident  LogisticRegression        0.790419       0.726190   0.618166  0.726190  0.639789
14     Glove-Basic-Accident       GradientBoost        0.997006       0.738095   0.672696  0.738095  0.680156
15     Glove-Basic-Accident                 SVM        0.739521       0.738095   0.544785  0.738095  0.626875
16     Glove-Basic-Accident             Bagging        0.949102       0.726190   0.660792  0.726190  0.662866
17     Glove-Basic-Accident        RandomForest        0.997006       0.738095   0.551348  0.738095  0.631199
18     Glove-Basic-Accident        DecisionTree        0.856287       0.607143   0.566527  0.607143  0.583639
19     Glove-Basic-Accident            AdaBoost        0.736527       0.726190   0.542456  0.726190  0.621018
20     Glove-Basic-Accident             XGBoost        0.997006       0.726190   0.555850  0.726190  0.629704
21     Glove-Basic-Accident      Gradient Boost        0.958084       0.726190   0.639194  0.726190  0.653061
22     TfIdf-Basic-Accident                 KNN        0.997006       0.726190   0.625000  0.726190  0.644151
23     TfIdf-Basic-Accident               Ridge        0.979042       0.738095   0.558072  0.738095  0.635582
24     TfIdf-Basic-Accident  LogisticRegression        0.793413       0.738095   0.551348  0.738095  0.631199
25     TfIdf-Basic-Accident       GradientBoost        0.997006       0.714286   0.560579  0.714286  0.628166
26     TfIdf-Basic-Accident                 SVM        0.739521       0.738095   0.544785  0.738095  0.626875
27     TfIdf-Basic-Accident             Bagging        0.949102       0.702381   0.551236  0.702381  0.617697
28     TfIdf-Basic-Accident        RandomForest        0.967066       0.702381   0.575015  0.702381  0.630616
29     TfIdf-Basic-Accident        DecisionTree        0.778443       0.654762   0.548584  0.654762  0.596989
30     TfIdf-Basic-Accident            AdaBoost        0.760479       0.726190   0.542456  0.726190  0.621018
31     TfIdf-Basic-Accident             XGBoost        0.997006       0.690476   0.579115  0.690476  0.629902
32     TfIdf-Basic-Accident      Gradient Boost        0.892216       0.726190   0.639194  0.726190  0.653061


combinedResultDF = pd.concat([combinedResultDF,runAllModelDefault(X_train=X_train_w2v_up,Y_train=y_train_w2v_up,X_test=X_test_w2v_pca,Y_test=y_test_w2v,descriptionOfData="Word2Vec-PCA&Upsampled-Accident")],ignore_index=True,sort=False)
combinedResultDF = pd.concat([combinedResultDF,runAllModelDefault(X_train=X_train_w2v_smote,Y_train=y_train_w2v_smote,X_test=X_test_w2v_pca,Y_test=y_test_w2v,descriptionOfData="Word2Vec-PCA&SMOTE-Accident")],ignore_index=True,sort=False)
combinedResultDF = pd.concat([combinedResultDF,runAllModelDefault(X_train=X_train_glove_up,Y_train=y_train_glove_up,X_test=X_test_glove_pca,Y_test=y_test_glove,descriptionOfData="Glove-PCA&Upsampled-Accident")],ignore_index=True,sort=False)
combinedResultDF = pd.concat([combinedResultDF,runAllModelDefault(X_train=X_train_glove_smote,Y_train=y_train_glove_smote,X_test=X_test_glove_pca,Y_test=y_test_glove,descriptionOfData="Glove-PCA&SMOTE-Accident")],ignore_index=True,sort=False)
combinedResultDF = pd.concat([combinedResultDF,runAllModelDefault(X_train=X_train_tfidf_up,Y_train=y_train_tfidf_up,X_test=X_test_tfidf_pca,Y_test=y_test_tfidf,descriptionOfData="TFIDF-PCA&Upsampled-Accident")],ignore_index=True,sort=False)
combinedResultDF = pd.concat([combinedResultDF,runAllModelDefault(X_train=X_train_tfidf_smote,Y_train=y_train_tfidf_smote,X_test=X_test_tfidf_pca,Y_test=y_test_tfidf,descriptionOfData="TFIDF-PCA&SMOTE-Accident")],ignore_index=True,sort=False)


combinedResultDF.to_csv('Model Performance Report.csv')


#combinedResultDF = pd.read_csv('drive/My Drive/AI ML - UT Austin/Project/Model Performance Report.csv')
combinedResultDF = pd.read_csv('Model Performance Report.csv')
combinedResultDF.drop(["Unnamed: 0"], axis=1, inplace=True)
combinedResultDF


combinedResult_Pot = pd.DataFrame(columns=['Description Of Data','ModelName','Train Accuracy','Test Accuracy','Precision','Recall','F1'])


#train test split based on potential accident level as target
X_train_w2v_p,X_test_w2v_p,y_train_w2v_p,y_test_w2v_p=split(X_w2v,y_w2v_pot)
X_train_glove_p,X_test_glove_p,y_train_glove_p,y_test_glove_p=split(X_glove,y_glove_pot)
X_train_tfidf_p,X_test_tfidf_p,y_train_tfidf_p,y_test_tfidf_p=split(X_tfidf,y_tfidf_pot)


combinedResult_Pot = pd.concat([combinedResult_Pot, runAllModelDefault(X_train=X_train_w2v_p,Y_train=y_train_w2v_p,X_test=X_test_w2v_p,Y_test=y_test_w2v_p,descriptionOfData="Potential Acc: Word2Vec-Basic-Accident")],ignore_index=True,sort=False)

====================================================================================================
Model :  KNN
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                      Description Of Data ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Word2Vec-Basic-Accident       KNN             1.0       0.333333   0.351785  0.333333  0.323133
====================================================================================================
Model :  Ridge
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                      Description Of Data ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Word2Vec-Basic-Accident       KNN        1.000000       0.333333   0.351785  0.333333  0.323133
1  Potential Acc: Word2Vec-Basic-Accident     Ridge        0.580838       0.345238   0.318958  0.345238  0.314690
====================================================================================================
Model :  LogisticRegression
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                      Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Word2Vec-Basic-Accident                 KNN        1.000000       0.333333   0.351785  0.333333  0.323133
1  Potential Acc: Word2Vec-Basic-Accident               Ridge        0.580838       0.345238   0.318958  0.345238  0.314690
2  Potential Acc: Word2Vec-Basic-Accident  LogisticRegression        0.574850       0.345238   0.331143  0.345238  0.319466
====================================================================================================
Model :  GradientBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                      Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Word2Vec-Basic-Accident                 KNN        1.000000       0.333333   0.351785  0.333333  0.323133
1  Potential Acc: Word2Vec-Basic-Accident               Ridge        0.580838       0.345238   0.318958  0.345238  0.314690
2  Potential Acc: Word2Vec-Basic-Accident  LogisticRegression        0.574850       0.345238   0.331143  0.345238  0.319466
3  Potential Acc: Word2Vec-Basic-Accident       GradientBoost        1.000000       0.428571   0.454034  0.428571  0.420352
====================================================================================================
Model :  SVM
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                      Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Word2Vec-Basic-Accident                 KNN        1.000000       0.333333   0.351785  0.333333  0.323133
1  Potential Acc: Word2Vec-Basic-Accident               Ridge        0.580838       0.345238   0.318958  0.345238  0.314690
2  Potential Acc: Word2Vec-Basic-Accident  LogisticRegression        0.574850       0.345238   0.331143  0.345238  0.319466
3  Potential Acc: Word2Vec-Basic-Accident       GradientBoost        1.000000       0.428571   0.454034  0.428571  0.420352
4  Potential Acc: Word2Vec-Basic-Accident                 SVM        0.479042       0.333333   0.270002  0.333333  0.238974
====================================================================================================
Model :  Bagging
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                      Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Word2Vec-Basic-Accident                 KNN        1.000000       0.333333   0.351785  0.333333  0.323133
1  Potential Acc: Word2Vec-Basic-Accident               Ridge        0.580838       0.345238   0.318958  0.345238  0.314690
2  Potential Acc: Word2Vec-Basic-Accident  LogisticRegression        0.574850       0.345238   0.331143  0.345238  0.319466
3  Potential Acc: Word2Vec-Basic-Accident       GradientBoost        1.000000       0.428571   0.454034  0.428571  0.420352
4  Potential Acc: Word2Vec-Basic-Accident                 SVM        0.479042       0.333333   0.270002  0.333333  0.238974
5  Potential Acc: Word2Vec-Basic-Accident             Bagging        0.985030       0.357143   0.355263  0.357143  0.350662
====================================================================================================
Model :  RandomForest
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                      Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Word2Vec-Basic-Accident                 KNN        1.000000       0.333333   0.351785  0.333333  0.323133
1  Potential Acc: Word2Vec-Basic-Accident               Ridge        0.580838       0.345238   0.318958  0.345238  0.314690
2  Potential Acc: Word2Vec-Basic-Accident  LogisticRegression        0.574850       0.345238   0.331143  0.345238  0.319466
3  Potential Acc: Word2Vec-Basic-Accident       GradientBoost        1.000000       0.428571   0.454034  0.428571  0.420352
4  Potential Acc: Word2Vec-Basic-Accident                 SVM        0.479042       0.333333   0.270002  0.333333  0.238974
5  Potential Acc: Word2Vec-Basic-Accident             Bagging        0.985030       0.357143   0.355263  0.357143  0.350662
6  Potential Acc: Word2Vec-Basic-Accident        RandomForest        1.000000       0.380952   0.395105  0.380952  0.354048
====================================================================================================
Model :  DecisionTree
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                      Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Word2Vec-Basic-Accident                 KNN        1.000000       0.333333   0.351785  0.333333  0.323133
1  Potential Acc: Word2Vec-Basic-Accident               Ridge        0.580838       0.345238   0.318958  0.345238  0.314690
2  Potential Acc: Word2Vec-Basic-Accident  LogisticRegression        0.574850       0.345238   0.331143  0.345238  0.319466
3  Potential Acc: Word2Vec-Basic-Accident       GradientBoost        1.000000       0.428571   0.454034  0.428571  0.420352
4  Potential Acc: Word2Vec-Basic-Accident                 SVM        0.479042       0.333333   0.270002  0.333333  0.238974
5  Potential Acc: Word2Vec-Basic-Accident             Bagging        0.985030       0.357143   0.355263  0.357143  0.350662
6  Potential Acc: Word2Vec-Basic-Accident        RandomForest        1.000000       0.380952   0.395105  0.380952  0.354048
7  Potential Acc: Word2Vec-Basic-Accident        DecisionTree        0.670659       0.392857   0.425240  0.392857  0.391922
====================================================================================================
Model :  AdaBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                      Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Word2Vec-Basic-Accident                 KNN        1.000000       0.333333   0.351785  0.333333  0.323133
1  Potential Acc: Word2Vec-Basic-Accident               Ridge        0.580838       0.345238   0.318958  0.345238  0.314690
2  Potential Acc: Word2Vec-Basic-Accident  LogisticRegression        0.574850       0.345238   0.331143  0.345238  0.319466
3  Potential Acc: Word2Vec-Basic-Accident       GradientBoost        1.000000       0.428571   0.454034  0.428571  0.420352
4  Potential Acc: Word2Vec-Basic-Accident                 SVM        0.479042       0.333333   0.270002  0.333333  0.238974
5  Potential Acc: Word2Vec-Basic-Accident             Bagging        0.985030       0.357143   0.355263  0.357143  0.350662
6  Potential Acc: Word2Vec-Basic-Accident        RandomForest        1.000000       0.380952   0.395105  0.380952  0.354048
7  Potential Acc: Word2Vec-Basic-Accident        DecisionTree        0.670659       0.392857   0.425240  0.392857  0.391922
8  Potential Acc: Word2Vec-Basic-Accident            AdaBoost        0.395210       0.333333   0.252778  0.333333  0.260916
====================================================================================================
Model :  XGBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


combinedResult_Pot = pd.concat([combinedResult_Pot, runAllModelDefault(X_train=X_train_glove_p,Y_train=y_train_glove_p,X_test=X_test_glove_p,Y_test=y_test_glove_p,descriptionOfData="Potential Acc: Glove-Basic-Accident")],ignore_index=True,sort=False)

====================================================================================================
Model :  KNN
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data ModelName  Train Accuracy  Test Accuracy  Precision    Recall       F1
0  Potential Acc: Glove-Basic-Accident       KNN             1.0       0.309524    0.34256  0.309524  0.29944
====================================================================================================
Model :  Ridge
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Glove-Basic-Accident       KNN        1.000000       0.309524   0.342560  0.309524  0.299440
1  Potential Acc: Glove-Basic-Accident     Ridge        0.769461       0.392857   0.395048  0.392857  0.382516
====================================================================================================
Model :  LogisticRegression
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Glove-Basic-Accident                 KNN        1.000000       0.309524   0.342560  0.309524  0.299440
1  Potential Acc: Glove-Basic-Accident               Ridge        0.769461       0.392857   0.395048  0.392857  0.382516
2  Potential Acc: Glove-Basic-Accident  LogisticRegression        0.685629       0.333333   0.349567  0.333333  0.332524
====================================================================================================
Model :  GradientBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Glove-Basic-Accident                 KNN        1.000000       0.309524   0.342560  0.309524  0.299440
1  Potential Acc: Glove-Basic-Accident               Ridge        0.769461       0.392857   0.395048  0.392857  0.382516
2  Potential Acc: Glove-Basic-Accident  LogisticRegression        0.685629       0.333333   0.349567  0.333333  0.332524
3  Potential Acc: Glove-Basic-Accident       GradientBoost        1.000000       0.488095   0.532038  0.488095  0.472378
====================================================================================================
Model :  SVM
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Glove-Basic-Accident                 KNN        1.000000       0.309524   0.342560  0.309524  0.299440
1  Potential Acc: Glove-Basic-Accident               Ridge        0.769461       0.392857   0.395048  0.392857  0.382516
2  Potential Acc: Glove-Basic-Accident  LogisticRegression        0.685629       0.333333   0.349567  0.333333  0.332524
3  Potential Acc: Glove-Basic-Accident       GradientBoost        1.000000       0.488095   0.532038  0.488095  0.472378
4  Potential Acc: Glove-Basic-Accident                 SVM        0.491018       0.345238   0.306831  0.345238  0.258694
====================================================================================================
Model :  Bagging
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Glove-Basic-Accident                 KNN        1.000000       0.309524   0.342560  0.309524  0.299440
1  Potential Acc: Glove-Basic-Accident               Ridge        0.769461       0.392857   0.395048  0.392857  0.382516
2  Potential Acc: Glove-Basic-Accident  LogisticRegression        0.685629       0.333333   0.349567  0.333333  0.332524
3  Potential Acc: Glove-Basic-Accident       GradientBoost        1.000000       0.488095   0.532038  0.488095  0.472378
4  Potential Acc: Glove-Basic-Accident                 SVM        0.491018       0.345238   0.306831  0.345238  0.258694
5  Potential Acc: Glove-Basic-Accident             Bagging        0.982036       0.333333   0.333013  0.333333  0.321007
====================================================================================================
Model :  RandomForest
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Glove-Basic-Accident                 KNN        1.000000       0.309524   0.342560  0.309524  0.299440
1  Potential Acc: Glove-Basic-Accident               Ridge        0.769461       0.392857   0.395048  0.392857  0.382516
2  Potential Acc: Glove-Basic-Accident  LogisticRegression        0.685629       0.333333   0.349567  0.333333  0.332524
3  Potential Acc: Glove-Basic-Accident       GradientBoost        1.000000       0.488095   0.532038  0.488095  0.472378
4  Potential Acc: Glove-Basic-Accident                 SVM        0.491018       0.345238   0.306831  0.345238  0.258694
5  Potential Acc: Glove-Basic-Accident             Bagging        0.982036       0.333333   0.333013  0.333333  0.321007
6  Potential Acc: Glove-Basic-Accident        RandomForest        1.000000       0.369048   0.333354  0.369048  0.318751
====================================================================================================
Model :  DecisionTree
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Glove-Basic-Accident                 KNN        1.000000       0.309524   0.342560  0.309524  0.299440
1  Potential Acc: Glove-Basic-Accident               Ridge        0.769461       0.392857   0.395048  0.392857  0.382516
2  Potential Acc: Glove-Basic-Accident  LogisticRegression        0.685629       0.333333   0.349567  0.333333  0.332524
3  Potential Acc: Glove-Basic-Accident       GradientBoost        1.000000       0.488095   0.532038  0.488095  0.472378
4  Potential Acc: Glove-Basic-Accident                 SVM        0.491018       0.345238   0.306831  0.345238  0.258694
5  Potential Acc: Glove-Basic-Accident             Bagging        0.982036       0.333333   0.333013  0.333333  0.321007
6  Potential Acc: Glove-Basic-Accident        RandomForest        1.000000       0.369048   0.333354  0.369048  0.318751
7  Potential Acc: Glove-Basic-Accident        DecisionTree        0.670659       0.380952   0.351380  0.380952  0.358427
====================================================================================================
Model :  AdaBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: Glove-Basic-Accident                 KNN        1.000000       0.309524   0.342560  0.309524  0.299440
1  Potential Acc: Glove-Basic-Accident               Ridge        0.769461       0.392857   0.395048  0.392857  0.382516
2  Potential Acc: Glove-Basic-Accident  LogisticRegression        0.685629       0.333333   0.349567  0.333333  0.332524
3  Potential Acc: Glove-Basic-Accident       GradientBoost        1.000000       0.488095   0.532038  0.488095  0.472378
4  Potential Acc: Glove-Basic-Accident                 SVM        0.491018       0.345238   0.306831  0.345238  0.258694
5  Potential Acc: Glove-Basic-Accident             Bagging        0.982036       0.333333   0.333013  0.333333  0.321007
6  Potential Acc: Glove-Basic-Accident        RandomForest        1.000000       0.369048   0.333354  0.369048  0.318751
7  Potential Acc: Glove-Basic-Accident        DecisionTree        0.670659       0.380952   0.351380  0.380952  0.358427
8  Potential Acc: Glove-Basic-Accident            AdaBoost        0.359281       0.309524   0.178995  0.309524  0.198791
====================================================================================================
Model :  XGBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


combinedResult_Pot = pd.concat([combinedResult_Pot, runAllModelDefault(X_train=X_train_tfidf_p,Y_train=y_train_tfidf_p,X_test=X_test_tfidf_p,Y_test=y_test_tfidf_p,descriptionOfData="Potential Acc: TFIDF-Basic-Accident")],ignore_index=True,sort=False)

====================================================================================================
Model :  KNN
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: TFIDF-Basic-Accident       KNN             1.0       0.357143   0.382876  0.357143  0.343491
====================================================================================================
Model :  Ridge
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: TFIDF-Basic-Accident       KNN             1.0       0.357143   0.382876  0.357143  0.343491
1  Potential Acc: TFIDF-Basic-Accident     Ridge             1.0       0.416667   0.439342  0.416667  0.396545
====================================================================================================
Model :  LogisticRegression
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: TFIDF-Basic-Accident                 KNN        1.000000       0.357143   0.382876  0.357143  0.343491
1  Potential Acc: TFIDF-Basic-Accident               Ridge        1.000000       0.416667   0.439342  0.416667  0.396545
2  Potential Acc: TFIDF-Basic-Accident  LogisticRegression        0.829341       0.428571   0.429549  0.428571  0.409535
====================================================================================================
Model :  GradientBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: TFIDF-Basic-Accident                 KNN        1.000000       0.357143   0.382876  0.357143  0.343491
1  Potential Acc: TFIDF-Basic-Accident               Ridge        1.000000       0.416667   0.439342  0.416667  0.396545
2  Potential Acc: TFIDF-Basic-Accident  LogisticRegression        0.829341       0.428571   0.429549  0.428571  0.409535
3  Potential Acc: TFIDF-Basic-Accident       GradientBoost        1.000000       0.452381   0.476030  0.452381  0.424106
====================================================================================================
Model :  SVM
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: TFIDF-Basic-Accident                 KNN        1.000000       0.357143   0.382876  0.357143  0.343491
1  Potential Acc: TFIDF-Basic-Accident               Ridge        1.000000       0.416667   0.439342  0.416667  0.396545
2  Potential Acc: TFIDF-Basic-Accident  LogisticRegression        0.829341       0.428571   0.429549  0.428571  0.409535
3  Potential Acc: TFIDF-Basic-Accident       GradientBoost        1.000000       0.452381   0.476030  0.452381  0.424106
4  Potential Acc: TFIDF-Basic-Accident                 SVM        0.494012       0.333333   0.268507  0.333333  0.237416
====================================================================================================
Model :  Bagging
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: TFIDF-Basic-Accident                 KNN        1.000000       0.357143   0.382876  0.357143  0.343491
1  Potential Acc: TFIDF-Basic-Accident               Ridge        1.000000       0.416667   0.439342  0.416667  0.396545
2  Potential Acc: TFIDF-Basic-Accident  LogisticRegression        0.829341       0.428571   0.429549  0.428571  0.409535
3  Potential Acc: TFIDF-Basic-Accident       GradientBoost        1.000000       0.452381   0.476030  0.452381  0.424106
4  Potential Acc: TFIDF-Basic-Accident                 SVM        0.494012       0.333333   0.268507  0.333333  0.237416
5  Potential Acc: TFIDF-Basic-Accident             Bagging        0.985030       0.357143   0.354722  0.357143  0.350317
====================================================================================================
Model :  RandomForest
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: TFIDF-Basic-Accident                 KNN        1.000000       0.357143   0.382876  0.357143  0.343491
1  Potential Acc: TFIDF-Basic-Accident               Ridge        1.000000       0.416667   0.439342  0.416667  0.396545
2  Potential Acc: TFIDF-Basic-Accident  LogisticRegression        0.829341       0.428571   0.429549  0.428571  0.409535
3  Potential Acc: TFIDF-Basic-Accident       GradientBoost        1.000000       0.452381   0.476030  0.452381  0.424106
4  Potential Acc: TFIDF-Basic-Accident                 SVM        0.494012       0.333333   0.268507  0.333333  0.237416
5  Potential Acc: TFIDF-Basic-Accident             Bagging        0.985030       0.357143   0.354722  0.357143  0.350317
6  Potential Acc: TFIDF-Basic-Accident        RandomForest        0.865269       0.369048   0.360937  0.369048  0.349767
====================================================================================================
Model :  DecisionTree
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: TFIDF-Basic-Accident                 KNN        1.000000       0.357143   0.382876  0.357143  0.343491
1  Potential Acc: TFIDF-Basic-Accident               Ridge        1.000000       0.416667   0.439342  0.416667  0.396545
2  Potential Acc: TFIDF-Basic-Accident  LogisticRegression        0.829341       0.428571   0.429549  0.428571  0.409535
3  Potential Acc: TFIDF-Basic-Accident       GradientBoost        1.000000       0.452381   0.476030  0.452381  0.424106
4  Potential Acc: TFIDF-Basic-Accident                 SVM        0.494012       0.333333   0.268507  0.333333  0.237416
5  Potential Acc: TFIDF-Basic-Accident             Bagging        0.985030       0.357143   0.354722  0.357143  0.350317
6  Potential Acc: TFIDF-Basic-Accident        RandomForest        0.865269       0.369048   0.360937  0.369048  0.349767
7  Potential Acc: TFIDF-Basic-Accident        DecisionTree        0.562874       0.321429   0.280764  0.321429  0.277755
====================================================================================================
Model :  AdaBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7ce4fb98eef0>
========================================
Models Performance Summary
========================================
                   Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Potential Acc: TFIDF-Basic-Accident                 KNN        1.000000       0.357143   0.382876  0.357143  0.343491
1  Potential Acc: TFIDF-Basic-Accident               Ridge        1.000000       0.416667   0.439342  0.416667  0.396545
2  Potential Acc: TFIDF-Basic-Accident  LogisticRegression        0.829341       0.428571   0.429549  0.428571  0.409535
3  Potential Acc: TFIDF-Basic-Accident       GradientBoost        1.000000       0.452381   0.476030  0.452381  0.424106
4  Potential Acc: TFIDF-Basic-Accident                 SVM        0.494012       0.333333   0.268507  0.333333  0.237416
5  Potential Acc: TFIDF-Basic-Accident             Bagging        0.985030       0.357143   0.354722  0.357143  0.350317
6  Potential Acc: TFIDF-Basic-Accident        RandomForest        0.865269       0.369048   0.360937  0.369048  0.349767
7  Potential Acc: TFIDF-Basic-Accident        DecisionTree        0.562874       0.321429   0.280764  0.321429  0.277755
8  Potential Acc: TFIDF-Basic-Accident            AdaBoost        0.440120       0.369048   0.402087  0.369048  0.339074
====================================================================================================
Model :  XGBoost
====================================================================================================


========================================
Classification - Heatmap
========================================


print("Combined results based on Potential accident level as target:")
combinedResult_Pot

Combined results based on Potential accident level as target:


def runTunedModel(model,X_train,Y_train,X_test,Y_test,descriptionOfData,nameOfTheModel):
    print("="*100)
    print("Model : ", nameOfTheModel)
    print("="*100)
    print("\n\n")
    model.fit(X_train,Y_train)
    y_predict = model.predict(X_test)
    trainAccuracyScore = model.score(X_train,Y_train)
    testAccuracyScore = model.score(X_test,Y_test)
    precisionScore = precision_score(Y_test,y_predict,average='weighted')
    recallScore = recall_score(Y_test,y_predict,average='weighted')
    f1Score = f1_score(Y_test,y_predict,average='weighted')
    print("=="*20)
    print('Classification - Heatmap')
    print("=="*20)
    print("\n")
    classificationMatrix = confusion_matrix(Y_test,y_predict)
    sns.heatmap(classificationMatrix,annot=True,fmt=".4f",xticklabels = ["I", "II", "III", "IV", "V"] , yticklabels = ["I", "II", "III", "IV", "V"] )
    plt.ylabel('Actual')
    plt.xlabel('Predicted')
    plt.show()
    print("\n")
    print("=="*20)
    classificationReport = classification_report(Y_test,y_predict)
    print('Classification - Report')
    print("=="*20)
    print("\n")
    print(classification_report)

    nameofModel=[]
    trainAccScore=[]
    testAccScore=[]
    precissionSc=[]
    recallSc=[]
    f1Sc=[]

    nameofModel.append(nameOfTheModel)
    trainAccScore.append(trainAccuracyScore)
    testAccScore.append(testAccuracyScore)
    precissionSc.append(precisionScore)
    recallSc.append(recallScore)
    f1Sc.append(f1Score)

    resultDF = pd.DataFrame({'Description Of Data':descriptionOfData,
                            'ModelName':nameofModel,
                            'Train Accuracy':trainAccScore,
                            'Test Accuracy':testAccScore,
                            'Precision':precissionSc,
                            'Recall':recallSc,
                            'F1':f1Sc})
    print("=="*20)
    print("Models Performance Summary")
    print("=="*20)
    print(resultDF)
    return resultDF


combinedResult_tune = pd.DataFrame(columns=['Description Of Data','ModelName','Train Accuracy','Test Accuracy','Precision','Recall','F1'])


# LOGISTIC REGRESSION-
# SVM Results for Word2Vec
#LR Hyper Param

# Define the parameter grid for Logistic Regression
param_grid = {
    'C': [0.001, 0.01, 0.1, 1, 10, 100],  # Regularization strength
    'penalty': ['l1', 'l2'],  # Regularization type
    'solver': ['liblinear', 'saga'],  # Optimization algorithm
    'max_iter': [100, 200, 300]  # Number of iterations
}

# Initialize RandomizedSearchCV
random_search_lr = RandomizedSearchCV(
    LogisticRegression(),
    param_grid,
    cv=4,
    n_iter=10,  # Number of parameter settings sampled
    random_state=7
)

# Collect results into a list
results = []
results.append(runTunedModel(model=random_search_lr, X_train=X_train_w2v, Y_train=y_train_w2v, X_test=X_test_w2v, Y_test=y_test_w2v, descriptionOfData="LR Hyper param tuning: Word2Vec-Basic-Accident", nameOfTheModel="LogisticRegression RandomizedSearchCV"))

====================================================================================================
Model :  LogisticRegression RandomizedSearchCV
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7be80f04ef80>
========================================
Models Performance Summary
========================================
                              Description Of Data  \
0  LR Hyper param tuning: Word2Vec-Basic-Accident   

                               ModelName  Train Accuracy  Test Accuracy  \
0  LogisticRegression RandomizedSearchCV        0.739521       0.738095   

   Precision    Recall        F1  
0   0.544785  0.738095  0.626875


# Logistic Regression Results for TfIdf
# Append the second result
results.append(runTunedModel(model=random_search_lr, X_train=X_train_tfidf, Y_train=y_train_tfidf, X_test=X_test_tfidf, Y_test=y_test_tfidf, descriptionOfData="LR Hyper param tuning: TfIdf-Basic-Accident", nameOfTheModel="LogisticRegression RandomizedSearchCV"))

====================================================================================================
Model :  LogisticRegression RandomizedSearchCV
====================================================================================================


========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7be80f04ef80>
========================================
Models Performance Summary
========================================
                           Description Of Data  \
0  LR Hyper param tuning: TfIdf-Basic-Accident   

                               ModelName  Train Accuracy  Test Accuracy  \
0  LogisticRegression RandomizedSearchCV        0.739521       0.738095   

   Precision    Recall        F1  
0   0.544785  0.738095  0.626875


# Concatenate and Display Results
# Concatenate results into a DataFrame
combinedResult_tune = pd.concat(results, ignore_index=True)

# Display the final DataFrame
print(combinedResult_tune)

                              Description Of Data  \
0  LR Hyper param tuning: Word2Vec-Basic-Accident   
1     LR Hyper param tuning: TfIdf-Basic-Accident   

                               ModelName  Train Accuracy  Test Accuracy  \
0  LogisticRegression RandomizedSearchCV        0.739521       0.738095   
1  LogisticRegression RandomizedSearchCV        0.739521       0.738095   

   Precision    Recall        F1  
0   0.544785  0.738095  0.626875  
1   0.544785  0.738095  0.626875


# SVM-
# Define the parameter grid for SVM
param_grid = {'C': [0.1, 1, 10, 100, 1000],
              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
              'kernel': ['linear', 'rbf', 'sigmoid', 'poly']}

# Initialize GridSearchCV
grid = GridSearchCV(SVC(), param_grid, cv=4, verbose=True)

# Collect results into a list
results = []
results.append(runTunedModel(model=grid, X_train=X_train_w2v, Y_train=y_train_w2v, X_test=X_test_w2v, Y_test=y_test_w2v, descriptionOfData="SVM Hyper param tuning: Word2Vec-Basic-Accident", nameOfTheModel="SVM GridSearchCV"))

====================================================================================================
Model :  SVM GridSearchCV
====================================================================================================


Fitting 4 folds for each of 100 candidates, totalling 400 fits
========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7be80f04ef80>
========================================
Models Performance Summary
========================================
                               Description Of Data         ModelName  \
0  SVM Hyper param tuning: Word2Vec-Basic-Accident  SVM GridSearchCV   

   Train Accuracy  Test Accuracy  Precision    Recall        F1  
0        0.739521       0.738095   0.544785  0.738095  0.626875


# SVM Results for TfIdf
results.append(runTunedModel(model=grid, X_train=X_train_tfidf, Y_train=y_train_tfidf, X_test=X_test_tfidf, Y_test=y_test_tfidf, descriptionOfData="SVM Hyper param tuning: TfIdf-Basic-Accident", nameOfTheModel="SVM GridSearchCV"))

====================================================================================================
Model :  SVM GridSearchCV
====================================================================================================


Fitting 4 folds for each of 100 candidates, totalling 400 fits
========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7be80f04ef80>
========================================
Models Performance Summary
========================================
                            Description Of Data         ModelName  \
0  SVM Hyper param tuning: TfIdf-Basic-Accident  SVM GridSearchCV   

   Train Accuracy  Test Accuracy  Precision    Recall        F1  
0        0.739521       0.738095   0.544785  0.738095  0.626875


# Concatenate and Display Results
combinedResult_tune = pd.concat(results, ignore_index=True)

# Set display width for better readability
pd.set_option('display.width', 1000)
print(combinedResult_tune)

                               Description Of Data         ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  SVM Hyper param tuning: Word2Vec-Basic-Accident  SVM GridSearchCV        0.739521       0.738095   0.544785  0.738095  0.626875
1     SVM Hyper param tuning: TfIdf-Basic-Accident  SVM GridSearchCV        0.739521       0.738095   0.544785  0.738095  0.626875


# Ridge Classifier Results for Word2Vec-
from sklearn.linear_model import RidgeClassifier
# Define the parameter grid for Ridge Classifier
param_grid = {'alpha': [0.1, 1, 10, 100, 1000],  # Regularization strength
              'solver': ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg']}

# Initialize GridSearchCV
grid = GridSearchCV(RidgeClassifier(), param_grid, cv=4, verbose=True)

# Collect results into a list
results = []
results.append(runTunedModel(model=grid, X_train=X_train_w2v, Y_train=y_train_w2v, X_test=X_test_w2v, Y_test=y_test_w2v, descriptionOfData="Ridge Hyper param tuning: Word2Vec-Basic-Accident", nameOfTheModel="Ridge GridSearchCV"))

====================================================================================================
Model :  Ridge GridSearchCV
====================================================================================================


Fitting 4 folds for each of 25 candidates, totalling 100 fits
========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7be80f04ef80>
========================================
Models Performance Summary
========================================
                                 Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Ridge Hyper param tuning: Word2Vec-Basic-Accident  Ridge GridSearchCV        0.739521       0.738095   0.544785  0.738095  0.626875


#  Ridge Classifier Results for TfIdf
# Append the second result

results.append(runTunedModel(model=grid, X_train=X_train_tfidf, Y_train=y_train_tfidf, X_test=X_test_tfidf, Y_test=y_test_tfidf, descriptionOfData="Ridge Hyper param tuning: TfIdf-Basic-Accident", nameOfTheModel="Ridge GridSearchCV"))

====================================================================================================
Model :  Ridge GridSearchCV
====================================================================================================


Fitting 4 folds for each of 25 candidates, totalling 100 fits
========================================
Classification - Heatmap
========================================


========================================
Classification - Report
========================================


<function classification_report at 0x7be80f04ef80>
========================================
Models Performance Summary
========================================
                              Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Ridge Hyper param tuning: TfIdf-Basic-Accident  Ridge GridSearchCV        0.742515       0.738095   0.544785  0.738095  0.626875


# Concatenate, Display, and Save Results

# Concatenate results into a DataFrame
combinedResult_tune = pd.concat(results, ignore_index=True)

# Set display width for better readability
pd.set_option('display.width', 1000)
print(combinedResult_tune)

# Save the DataFrame to a CSV file
combinedResult_tune.to_csv('Model Performance Report tuned.csv', index=False)

                                 Description Of Data           ModelName  Train Accuracy  Test Accuracy  Precision    Recall        F1
0  Ridge Hyper param tuning: Word2Vec-Basic-Accident  Ridge GridSearchCV        0.739521       0.738095   0.544785  0.738095  0.626875
1     Ridge Hyper param tuning: TfIdf-Basic-Accident  Ridge GridSearchCV        0.742515       0.738095   0.544785  0.738095  0.626875


!pip install keras-tuner

Collecting keras-tuner
  Downloading keras_tuner-1.4.7-py3-none-any.whl.metadata (5.4 kB)
Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (3.4.1)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (24.1)
Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.32.3)
Collecting kt-legacy (from keras-tuner)
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import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
import keras_tuner as kt
from tensorflow.keras.preprocessing.text import Tokenizer
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, recall_score, precision_score, classification_report
from sklearn.utils import resample
from sklearn.model_selection import train_test_split,GridSearchCV,RandomizedSearchCV
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input, Dropout,BatchNormalization,LSTM
from tensorflow.keras import regularizers, backend, optimizers
from scikeras.wrappers import KerasClassifier
from tensorflow.keras.utils import to_categorical
from keras.constraints import unit_norm
from keras.regularizers import l1, l2, l1_l2
from keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
from keras.models import model_from_json
from sklearn import metrics
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.layers import SimpleRNN
from scikeras.wrappers import KerasClassifier


def split(X,y):
    # Initial split into training (80%) and testing (20%)
    if np.min(np.bincount(y)) < 2:
    #Remove stratification if y has single sample - required in case of potential accident level V
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
    else:
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, stratify=y, random_state=42)
    return X_train,X_test,y_train,y_test


#Alternate Read for Files from Local PC
df_w2v_import = pd.read_excel('/content/drive/My Drive/AIML/feature_df_final_word2vec.xlsx')
df_glove_import = pd.read_excel('/content/drive/My Drive/AIML/feature_df_final_glove.xlsx')
df_tfidf_import= pd.read_excel('/content/drive/My Drive/AIML/feature_df_final_tfidf.xlsx')


#Deleting Unnamed column
df_w2v_import = df_w2v_import.drop(df_w2v_import.columns[[0]],axis = 1)
df_glove_import = df_glove_import.drop(df_glove_import.columns[[0]],axis = 1)
df_tfidf_import = df_tfidf_import.drop(df_tfidf_import.columns[[0]],axis = 1)

X_w2v = df_w2v_import.drop(['Accident Level','Potential Accident Level'], axis = 1)
X_glove = df_glove_import.drop(['Accident Level','Potential Accident Level'], axis = 1)
X_tfidf = df_tfidf_import.drop(['Accident Level','Potential Accident Level'], axis = 1)

y_w2v = df_w2v_import['Accident Level']
y_glove = df_glove_import['Accident Level']
y_tfidf = df_tfidf_import['Accident Level']


# Splitting on Basic data
X_train_w2v,X_test_w2v,y_train_w2v,y_test_w2v=split(X_w2v,y_w2v)
X_train_glove,X_test_glove,y_train_glove,y_test_glove=split(X_glove,y_glove)
X_train_tfidf,X_test_tfidf,y_train_tfidf,y_test_tfidf=split(X_tfidf,y_tfidf)


print(f'Shape of Basic W2V Train and Test Data --- \nX_train_w2v - {X_train_w2v.shape} \nX_test_w2v - {X_test_w2v.shape} \ny_train_w2v - {y_train_w2v.shape} \ny_test_w2v - {y_test_w2v.shape}')

Shape of Basic W2V Train and Test Data --- 
X_train_w2v - (334, 151) 
X_test_w2v - (84, 151) 
y_train_w2v - (334,) 
y_test_w2v - (84,)


#Categorical conversion
y_train_w2v_cat = to_categorical(y_train_w2v,5)
y_test_w2v_cat = to_categorical(y_test_w2v,5)

y_train_glove_cat = to_categorical(y_train_glove,5)
y_test_glove_cat = to_categorical(y_test_glove,5)

y_train_tfidf_cat = to_categorical(y_train_tfidf,5)
y_test_tfidf_cat = to_categorical(y_test_tfidf,5)


y_train_w2v_cat.shape

(334, 5)


y_train_w2v_cat[1]

array([0., 1., 0., 0., 0.])


import sklearn
from sklearn import metrics

def evaluationScoresandPlots(modelNN,historyNN,Accuracy,ValidationAccuracy,DataDesc,ModelDesc,X_train,Y_train,X_test,Y_test):

    print('Train and Validation Loss')
    # Capturing learning history per epoch
    hist  = pd.DataFrame(historyNN.history)
    hist['epoch'] = historyNN.epoch
    # Plotting accuracy at different epochs
    plt.plot(hist['loss'])
    plt.plot(hist['val_loss'])
    plt.legend(("Train-loss" , "Valid-loss") , loc =0)
    plt.title(f'Training and Validation Loss for data :{DataDesc} in {ModelDesc}')
    plt.show()

    print('Train and Validation Accuracy')
    # Capturing learning history per epoch
    hist  = pd.DataFrame(historyNN.history)
    hist['epoch'] = historyNN.epoch

    # Plotting accuracy at different epochs
    plt.plot(hist[Accuracy])
    plt.plot(hist[ValidationAccuracy])
    plt.legend(("Train-accuracy" , "Valid-accuracy") , loc =0)
    plt.title(f'Training and Validation Accuracy for data :{DataDesc} in {ModelDesc}')
    plt.show()

    y_test_arg=np.argmax(Y_test,axis=1)
    Y_pred = np.argmax(modelNN.predict(X_test),axis=1)
    print('Confusion Matrix')

    cm=confusion_matrix(y_test_arg,Y_pred, labels=list(range(0,5)))
    cm_df = pd.DataFrame(cm,
                        index = list(range(0,5)),
                        columns = list(range(0,5)))

    plt.figure(figsize=(10,7))
    sns.heatmap(cm_df,annot=True,fmt='d')
    plt.xlabel('Predicted')
    plt.ylabel('Truth')
    plt.show()

    #y_pred=modelNN.predict(X_test)
    #y_pred = (y_pred > 0.5)
    #Accuracy as per the classification report
    cr=metrics.classification_report(y_test_arg,Y_pred)
    print(cr)

    # evaluate the keras model
    _, train_accuracy = modelNN.evaluate(X_train, Y_train, batch_size=8, verbose=0)
    _, test_accuracy = modelNN.evaluate(X_test, Y_test, batch_size=8, verbose=0)

    train_accuracy_score = round(train_accuracy*100,2)
    test_accuracy_score = round(test_accuracy*100,2)

    f1_score_value = round(sklearn.metrics.f1_score(y_test_arg,Y_pred,average='weighted')*100,2)

    tempResult = pd.DataFrame({'Data Description':DataDesc,
                                'Model Description':ModelDesc,
                                'Train Accuracy':train_accuracy_score,
                                'Test Accuracy':test_accuracy_score,
                                'F1 Score':f1_score_value
                                }, index=[0])

    print(f'Result Summary \n --------------------- \n :{tempResult}\n\n')
    return tempResult


backend.clear_session()
#Fixing the seed for random number generators so that we can ensure we receive the same output everytime
np.random.seed(7)
import random
random.seed(7)
tf.random.set_seed(7)
combinedNNModelResult = pd.DataFrame(columns=['Data Description','Model Description','Train Accuracy','Test Accuracy', 'F1 Score'])


modelDesc = 'Basic Model with 1 layer'
dataDesc =  'W2V Basic'
# Initializing the ANN
modelw2v_basic = Sequential()

# This adds the input layer
modelw2v_basic.add(Dense(activation = 'relu', input_dim = X_train_w2v.shape[1], units=64))

#Add 1st hidden layer
modelw2v_basic.add(Dense(32, activation='relu'))

# Adding the output layer
modelw2v_basic.add(Dense(5, activation = 'softmax'))

# Compile the model
modelw2v_basic.compile(optimizer=(optimizers.Adam(learning_rate=1e-3)), loss='categorical_crossentropy', metrics=['accuracy'])
modelw2v_basic.summary()

history=modelw2v_basic.fit(X_train_w2v, y_train_w2v_cat,
          validation_split=0.2,
          epochs=50,
          batch_size=32,verbose=1)

combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=modelw2v_basic,historyNN=history,Accuracy='accuracy',ValidationAccuracy='val_accuracy',DataDesc=dataDesc,ModelDesc=modelDesc,X_train=X_train_w2v,Y_train=y_train_w2v_cat,X_test=X_test_w2v,Y_test=y_test_w2v_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense (Dense)                        │ (None, 64)                  │           9,728 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 (Dense)                      │ (None, 32)                  │           2,080 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_2 (Dense)                      │ (None, 5)                   │             165 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 11,973 (46.77 KB)

 Trainable params: 11,973 (46.77 KB)

 Non-trainable params: 0 (0.00 B)

Epoch 1/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step - accuracy: 0.0775 - loss: 1.7080 - val_accuracy: 0.4030 - val_loss: 1.4161
Epoch 2/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.5864 - loss: 1.2989 - val_accuracy: 0.7164 - val_loss: 1.1690
Epoch 3/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 1.1101 - val_accuracy: 0.7313 - val_loss: 1.0640
Epoch 4/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 1.0348 - val_accuracy: 0.7313 - val_loss: 1.0237
Epoch 5/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7178 - loss: 1.0076 - val_accuracy: 0.7313 - val_loss: 0.9984
Epoch 6/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 0.9879 - val_accuracy: 0.7313 - val_loss: 0.9699
Epoch 7/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7178 - loss: 0.9664 - val_accuracy: 0.7313 - val_loss: 0.9465
Epoch 8/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.9494 - val_accuracy: 0.7313 - val_loss: 0.9307
Epoch 9/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7178 - loss: 0.9363 - val_accuracy: 0.7313 - val_loss: 0.9186
Epoch 10/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7178 - loss: 0.9250 - val_accuracy: 0.7313 - val_loss: 0.9112
Epoch 11/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.9152 - val_accuracy: 0.7313 - val_loss: 0.9069
Epoch 12/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.9065 - val_accuracy: 0.7313 - val_loss: 0.9064
Epoch 13/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7178 - loss: 0.8980 - val_accuracy: 0.7313 - val_loss: 0.9071
Epoch 14/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 0.8906 - val_accuracy: 0.7313 - val_loss: 0.9080
Epoch 15/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8837 - val_accuracy: 0.7313 - val_loss: 0.9090
Epoch 16/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8767 - val_accuracy: 0.7313 - val_loss: 0.9093
Epoch 17/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8704 - val_accuracy: 0.7313 - val_loss: 0.9108
Epoch 18/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8641 - val_accuracy: 0.7313 - val_loss: 0.9112
Epoch 19/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8575 - val_accuracy: 0.7313 - val_loss: 0.9129
Epoch 20/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8508 - val_accuracy: 0.7313 - val_loss: 0.9130
Epoch 21/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.7178 - loss: 0.8440 - val_accuracy: 0.7313 - val_loss: 0.9127
Epoch 22/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8368 - val_accuracy: 0.7313 - val_loss: 0.9140
Epoch 23/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8300 - val_accuracy: 0.7313 - val_loss: 0.9161
Epoch 24/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8236 - val_accuracy: 0.7313 - val_loss: 0.9168
Epoch 25/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8155 - val_accuracy: 0.7313 - val_loss: 0.9175
Epoch 26/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7178 - loss: 0.8081 - val_accuracy: 0.7313 - val_loss: 0.9194
Epoch 27/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7178 - loss: 0.8009 - val_accuracy: 0.7313 - val_loss: 0.9195
Epoch 28/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.7933 - val_accuracy: 0.7313 - val_loss: 0.9197
Epoch 29/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 0.7867 - val_accuracy: 0.7313 - val_loss: 0.9214
Epoch 30/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7270 - loss: 0.7784 - val_accuracy: 0.7313 - val_loss: 0.9220
Epoch 31/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7270 - loss: 0.7708 - val_accuracy: 0.7313 - val_loss: 0.9249
Epoch 32/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7278 - loss: 0.7635 - val_accuracy: 0.7313 - val_loss: 0.9247
Epoch 33/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7278 - loss: 0.7562 - val_accuracy: 0.7313 - val_loss: 0.9264
Epoch 34/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7285 - loss: 0.7488 - val_accuracy: 0.7313 - val_loss: 0.9270
Epoch 35/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7285 - loss: 0.7401 - val_accuracy: 0.7313 - val_loss: 0.9304
Epoch 36/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7285 - loss: 0.7335 - val_accuracy: 0.7313 - val_loss: 0.9310
Epoch 37/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.7346 - loss: 0.7248 - val_accuracy: 0.7313 - val_loss: 0.9328
Epoch 38/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7346 - loss: 0.7171 - val_accuracy: 0.7313 - val_loss: 0.9371
Epoch 39/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7439 - loss: 0.7094 - val_accuracy: 0.7313 - val_loss: 0.9370
Epoch 40/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7439 - loss: 0.7010 - val_accuracy: 0.7313 - val_loss: 0.9415
Epoch 41/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7455 - loss: 0.6942 - val_accuracy: 0.7313 - val_loss: 0.9423
Epoch 42/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7500 - loss: 0.6859 - val_accuracy: 0.7164 - val_loss: 0.9451
Epoch 43/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7500 - loss: 0.6781 - val_accuracy: 0.7164 - val_loss: 0.9496
Epoch 44/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7580 - loss: 0.6713 - val_accuracy: 0.7164 - val_loss: 0.9522
Epoch 45/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7580 - loss: 0.6614 - val_accuracy: 0.7164 - val_loss: 0.9580
Epoch 46/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7601 - loss: 0.6539 - val_accuracy: 0.7164 - val_loss: 0.9604
Epoch 47/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7629 - loss: 0.6457 - val_accuracy: 0.7164 - val_loss: 0.9646
Epoch 48/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7674 - loss: 0.6376 - val_accuracy: 0.7164 - val_loss: 0.9698
Epoch 49/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7674 - loss: 0.6302 - val_accuracy: 0.7164 - val_loss: 0.9737
Epoch 50/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7674 - loss: 0.6211 - val_accuracy: 0.7164 - val_loss: 0.9779
Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 37ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.75      1.00      0.86        62
           1       0.00      0.00      0.00         8
           2       1.00      0.17      0.29         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.75        84
   macro avg       0.35      0.23      0.23        84
weighted avg       0.62      0.75      0.65        84

Result Summary 
 --------------------- 
 :  Data Description         Model Description  Train Accuracy  Test Accuracy  \
0        W2V Basic  Basic Model with 1 layer           78.44           75.0   

   F1 Score  
0     65.16  


Collating Results : 
-----------------------
  Data Description         Model Description  Train Accuracy  Test Accuracy  F1 Score
0        W2V Basic  Basic Model with 1 layer           78.44           75.0     65.16


modelDesc = 'Basic Model with 1 layer'
dataDesc =  'Glove Basic Data'

# Initializing the ANN
model_basic = Sequential()

# This adds the input layer
model_basic.add(Dense(activation = 'relu', input_dim = X_train_glove.shape[1], units=64))
#Add 1st hidden layer
model_basic.add(Dense(32, activation='relu'))
# Adding the output layer
model_basic.add(Dense(5, activation = 'softmax'))
# Compile the model
model_basic.compile(optimizer=(optimizers.Adam(learning_rate=1e-3)), loss='categorical_crossentropy', metrics=['accuracy'])
model_basic.summary()

history=model_basic.fit(X_train_glove, y_train_glove_cat,
          validation_split=0.2,
          epochs=50,
          batch_size=32,verbose=1)

combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=model_basic,historyNN=history,Accuracy='accuracy',ValidationAccuracy='val_accuracy',DataDesc=dataDesc,ModelDesc=modelDesc,X_train=X_train_glove,Y_train=y_train_glove_cat,X_test=X_test_glove,Y_test=y_test_glove_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Model: "sequential_1"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_3 (Dense)                      │ (None, 64)                  │           9,728 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_4 (Dense)                      │ (None, 32)                  │           2,080 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_5 (Dense)                      │ (None, 5)                   │             165 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 11,973 (46.77 KB)

 Trainable params: 11,973 (46.77 KB)

 Non-trainable params: 0 (0.00 B)

Epoch 1/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 2s 53ms/step - accuracy: 0.1896 - loss: 1.5895 - val_accuracy: 0.7313 - val_loss: 1.2517
Epoch 2/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.7178 - loss: 1.1378 - val_accuracy: 0.7313 - val_loss: 1.0175
Epoch 3/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7178 - loss: 0.9754 - val_accuracy: 0.7313 - val_loss: 0.9973
Epoch 4/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7178 - loss: 0.9695 - val_accuracy: 0.7313 - val_loss: 0.9688
Epoch 5/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 0.9437 - val_accuracy: 0.7313 - val_loss: 0.9473
Epoch 6/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7178 - loss: 0.9221 - val_accuracy: 0.7313 - val_loss: 0.9365
Epoch 7/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 0.9055 - val_accuracy: 0.7313 - val_loss: 0.9273
Epoch 8/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8920 - val_accuracy: 0.7313 - val_loss: 0.9172
Epoch 9/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8798 - val_accuracy: 0.7313 - val_loss: 0.9064
Epoch 10/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7178 - loss: 0.8659 - val_accuracy: 0.7313 - val_loss: 0.9002
Epoch 11/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7178 - loss: 0.8529 - val_accuracy: 0.7313 - val_loss: 0.8957
Epoch 12/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8418 - val_accuracy: 0.7313 - val_loss: 0.8918
Epoch 13/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.8297 - val_accuracy: 0.7313 - val_loss: 0.8896
Epoch 14/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.7178 - loss: 0.8179 - val_accuracy: 0.7313 - val_loss: 0.8877
Epoch 15/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7178 - loss: 0.8051 - val_accuracy: 0.7313 - val_loss: 0.8859
Epoch 16/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 0.7926 - val_accuracy: 0.7313 - val_loss: 0.8846
Epoch 17/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7178 - loss: 0.7796 - val_accuracy: 0.7313 - val_loss: 0.8831
Epoch 18/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7185 - loss: 0.7656 - val_accuracy: 0.7313 - val_loss: 0.8842
Epoch 19/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7185 - loss: 0.7518 - val_accuracy: 0.7313 - val_loss: 0.8835
Epoch 20/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7185 - loss: 0.7372 - val_accuracy: 0.7313 - val_loss: 0.8849
Epoch 21/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7185 - loss: 0.7209 - val_accuracy: 0.7313 - val_loss: 0.8856
Epoch 22/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7201 - loss: 0.7061 - val_accuracy: 0.7313 - val_loss: 0.8853
Epoch 23/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7201 - loss: 0.6882 - val_accuracy: 0.7313 - val_loss: 0.8851
Epoch 24/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7225 - loss: 0.6726 - val_accuracy: 0.7164 - val_loss: 0.8852
Epoch 25/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7225 - loss: 0.6542 - val_accuracy: 0.7164 - val_loss: 0.8879
Epoch 26/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7270 - loss: 0.6380 - val_accuracy: 0.7164 - val_loss: 0.8861
Epoch 27/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7331 - loss: 0.6173 - val_accuracy: 0.7164 - val_loss: 0.8900
Epoch 28/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7384 - loss: 0.6022 - val_accuracy: 0.7015 - val_loss: 0.8895
Epoch 29/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7499 - loss: 0.5796 - val_accuracy: 0.7015 - val_loss: 0.8988
Epoch 30/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7599 - loss: 0.5643 - val_accuracy: 0.7015 - val_loss: 0.8973
Epoch 31/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7644 - loss: 0.5415 - val_accuracy: 0.7015 - val_loss: 0.9068
Epoch 32/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7910 - loss: 0.5207 - val_accuracy: 0.7015 - val_loss: 0.9155
Epoch 33/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8022 - loss: 0.5025 - val_accuracy: 0.7015 - val_loss: 0.9247
Epoch 34/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.8043 - loss: 0.4848 - val_accuracy: 0.7015 - val_loss: 0.9287
Epoch 35/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.8070 - loss: 0.4630 - val_accuracy: 0.7015 - val_loss: 0.9388
Epoch 36/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8299 - loss: 0.4458 - val_accuracy: 0.7015 - val_loss: 0.9460
Epoch 37/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8299 - loss: 0.4287 - val_accuracy: 0.7313 - val_loss: 0.9522
Epoch 38/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8553 - loss: 0.4096 - val_accuracy: 0.7164 - val_loss: 0.9620
Epoch 39/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8754 - loss: 0.3939 - val_accuracy: 0.7313 - val_loss: 0.9718
Epoch 40/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8867 - loss: 0.3772 - val_accuracy: 0.7463 - val_loss: 0.9845
Epoch 41/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.8878 - loss: 0.3630 - val_accuracy: 0.7463 - val_loss: 0.9876
Epoch 42/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9002 - loss: 0.3454 - val_accuracy: 0.7463 - val_loss: 1.0045
Epoch 43/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.8981 - loss: 0.3309 - val_accuracy: 0.7463 - val_loss: 1.0167
Epoch 44/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9018 - loss: 0.3163 - val_accuracy: 0.7463 - val_loss: 1.0288
Epoch 45/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.8997 - loss: 0.3029 - val_accuracy: 0.7463 - val_loss: 1.0417
Epoch 46/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9091 - loss: 0.2882 - val_accuracy: 0.7463 - val_loss: 1.0528
Epoch 47/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9091 - loss: 0.2764 - val_accuracy: 0.7463 - val_loss: 1.0693
Epoch 48/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9398 - loss: 0.2638 - val_accuracy: 0.7313 - val_loss: 1.0797
Epoch 49/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9579 - loss: 0.2522 - val_accuracy: 0.7313 - val_loss: 1.0975
Epoch 50/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9579 - loss: 0.2408 - val_accuracy: 0.7313 - val_loss: 1.1079
Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.78      0.94      0.85        62
           1       0.33      0.12      0.18         8
           2       0.40      0.33      0.36         6
           3       0.50      0.17      0.25         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.40      0.31      0.33        84
weighted avg       0.67      0.74      0.69        84

Result Summary 
 --------------------- 
 :   Data Description         Model Description  Train Accuracy  Test Accuracy  F1 Score
0  Glove Basic Data  Basic Model with 1 layer           91.02          73.81     69.07


Collating Results : 
-----------------------
   Data Description         Model Description  Train Accuracy  Test Accuracy  F1 Score
0         W2V Basic  Basic Model with 1 layer           78.44          75.00     65.16
0  Glove Basic Data  Basic Model with 1 layer           91.02          73.81     69.07


modelDesc = 'Basic Model with 1 layer'
dataDesc =  'TFIDF Basic Data'
# Initializing the ANN
model_basic = Sequential()
# This adds the input layer (by specifying input dimension) AND the first hidden layer (units)
model_basic.add(Dense(activation = 'relu', input_dim = X_train_tfidf.shape[1], units=64))
#Add 1st hidden layer
model_basic.add(Dense(32, activation='relu'))
# Adding the output layer
model_basic.add(Dense(5, activation = 'softmax'))
## Compile the model
model_basic.compile(optimizer=(optimizers.Adam(learning_rate=1e-3)), loss='categorical_crossentropy', metrics=['accuracy'])
model_basic.summary()

history=model_basic.fit(X_train_tfidf, y_train_tfidf_cat,
          validation_split=0.2,
          epochs=50,
          batch_size=32,verbose=1)

combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=model_basic,historyNN=history,Accuracy='accuracy',ValidationAccuracy='val_accuracy',DataDesc=dataDesc,ModelDesc=modelDesc,X_train=X_train_tfidf,Y_train=y_train_tfidf_cat,X_test=X_test_tfidf,Y_test=y_test_tfidf_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Model: "sequential_2"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_6 (Dense)                      │ (None, 64)                  │         181,696 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_7 (Dense)                      │ (None, 32)                  │           2,080 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_8 (Dense)                      │ (None, 5)                   │             165 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 183,941 (718.52 KB)

 Trainable params: 183,941 (718.52 KB)

 Non-trainable params: 0 (0.00 B)

Epoch 1/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 2s 42ms/step - accuracy: 0.2120 - loss: 1.6608 - val_accuracy: 0.7313 - val_loss: 1.4348
Epoch 2/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 1.3833 - val_accuracy: 0.7313 - val_loss: 1.2569
Epoch 3/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7178 - loss: 1.1979 - val_accuracy: 0.7313 - val_loss: 1.0875
Epoch 4/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7178 - loss: 1.0292 - val_accuracy: 0.7313 - val_loss: 0.9990
Epoch 5/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7178 - loss: 0.9511 - val_accuracy: 0.7313 - val_loss: 0.9841
Epoch 6/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 0.9153 - val_accuracy: 0.7313 - val_loss: 0.9577
Epoch 7/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 0.8724 - val_accuracy: 0.7313 - val_loss: 0.9412
Epoch 8/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7178 - loss: 0.8347 - val_accuracy: 0.7313 - val_loss: 0.9266
Epoch 9/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7178 - loss: 0.7924 - val_accuracy: 0.7313 - val_loss: 0.9144
Epoch 10/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.7178 - loss: 0.7480 - val_accuracy: 0.7313 - val_loss: 0.9036
Epoch 11/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.7178 - loss: 0.7020 - val_accuracy: 0.7313 - val_loss: 0.8955
Epoch 12/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.7178 - loss: 0.6513 - val_accuracy: 0.7313 - val_loss: 0.8892
Epoch 13/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.7185 - loss: 0.5980 - val_accuracy: 0.7313 - val_loss: 0.8860
Epoch 14/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.7365 - loss: 0.5432 - val_accuracy: 0.7313 - val_loss: 0.8851
Epoch 15/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.7442 - loss: 0.4894 - val_accuracy: 0.7313 - val_loss: 0.8872
Epoch 16/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.7744 - loss: 0.4360 - val_accuracy: 0.7313 - val_loss: 0.8919
Epoch 17/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.8209 - loss: 0.3845 - val_accuracy: 0.7313 - val_loss: 0.8951
Epoch 18/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.8909 - loss: 0.3351 - val_accuracy: 0.7313 - val_loss: 0.9097
Epoch 19/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9536 - loss: 0.2890 - val_accuracy: 0.7313 - val_loss: 0.9263
Epoch 20/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9692 - loss: 0.2475 - val_accuracy: 0.7313 - val_loss: 0.9330
Epoch 21/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9938 - loss: 0.2086 - val_accuracy: 0.7313 - val_loss: 0.9552
Epoch 22/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9938 - loss: 0.1745 - val_accuracy: 0.7313 - val_loss: 0.9766
Epoch 23/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9938 - loss: 0.1450 - val_accuracy: 0.7313 - val_loss: 0.9866
Epoch 24/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9938 - loss: 0.1205 - val_accuracy: 0.7313 - val_loss: 1.0117
Epoch 25/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9938 - loss: 0.1002 - val_accuracy: 0.7313 - val_loss: 1.0226
Epoch 26/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.9938 - loss: 0.0844 - val_accuracy: 0.7313 - val_loss: 1.0458
Epoch 27/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9938 - loss: 0.0717 - val_accuracy: 0.7164 - val_loss: 1.0575
Epoch 28/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9938 - loss: 0.0619 - val_accuracy: 0.7164 - val_loss: 1.0772
Epoch 29/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9938 - loss: 0.0539 - val_accuracy: 0.7164 - val_loss: 1.0931
Epoch 30/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9965 - loss: 0.0474 - val_accuracy: 0.7164 - val_loss: 1.1050
Epoch 31/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9965 - loss: 0.0423 - val_accuracy: 0.7164 - val_loss: 1.1235
Epoch 32/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0381 - val_accuracy: 0.7164 - val_loss: 1.1319
Epoch 33/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9965 - loss: 0.0348 - val_accuracy: 0.7164 - val_loss: 1.1503
Epoch 34/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9965 - loss: 0.0321 - val_accuracy: 0.7164 - val_loss: 1.1544
Epoch 35/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9965 - loss: 0.0299 - val_accuracy: 0.7164 - val_loss: 1.1762
Epoch 36/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0278 - val_accuracy: 0.7164 - val_loss: 1.1741
Epoch 37/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.9965 - loss: 0.0264 - val_accuracy: 0.7164 - val_loss: 1.2041
Epoch 38/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0247 - val_accuracy: 0.7164 - val_loss: 1.1851
Epoch 39/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9965 - loss: 0.0240 - val_accuracy: 0.7164 - val_loss: 1.2388
Epoch 40/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9965 - loss: 0.0226 - val_accuracy: 0.7164 - val_loss: 1.1884
Epoch 41/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0228 - val_accuracy: 0.7164 - val_loss: 1.2714
Epoch 42/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0212 - val_accuracy: 0.7164 - val_loss: 1.2061
Epoch 43/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0211 - val_accuracy: 0.7164 - val_loss: 1.2767
Epoch 44/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9965 - loss: 0.0195 - val_accuracy: 0.7164 - val_loss: 1.2400
Epoch 45/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0191 - val_accuracy: 0.7164 - val_loss: 1.2812
Epoch 46/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0181 - val_accuracy: 0.7164 - val_loss: 1.2571
Epoch 47/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0178 - val_accuracy: 0.7164 - val_loss: 1.2956
Epoch 48/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9965 - loss: 0.0170 - val_accuracy: 0.7164 - val_loss: 1.2687
Epoch 49/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.9965 - loss: 0.0169 - val_accuracy: 0.7164 - val_loss: 1.3105
Epoch 50/50
9/9 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.9965 - loss: 0.0162 - val_accuracy: 0.7164 - val_loss: 1.2762
Train and Validation Loss

Train and Validation Accuracy

WARNING:tensorflow:5 out of the last 7 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f71fd29c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

1/3 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step

WARNING:tensorflow:6 out of the last 9 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7f71fd29c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.


def modelNN_withDropoutsAndNormalization(X_train,Y_train):
    param = 1e-4

    # define the model
    model = Sequential()

    model.add(Dense(150, input_dim=X_train.shape[1], activation='relu', kernel_initializer='he_uniform', kernel_regularizer=l2(param),
                    kernel_constraint=unit_norm()))
    model.add(Dropout(0.2))
    model.add(BatchNormalization())
    model.add(Dense(100, activation='relu', kernel_initializer='he_uniform', kernel_regularizer=l2(param),
                    kernel_constraint=unit_norm()))
    model.add(Dropout(0.5))
    model.add(BatchNormalization())
    model.add(Dense(50, activation='relu', kernel_initializer='he_uniform', kernel_regularizer=l2(param),
                    kernel_constraint=unit_norm()))
    model.add(Dropout(0.5))
    model.add(BatchNormalization())
    model.add(Dense(5, activation='softmax', kernel_regularizer=l2(param),
                    kernel_constraint=unit_norm()))
    model.summary()

    model.compile(loss='categorical_crossentropy', optimizer=optimizers.SGD(learning_rate=0.001, momentum=0.9), metrics=['categorical_accuracy'])
    # Use earlystopping
    callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=7, min_delta=1E-3)
    rlrp = ReduceLROnPlateau(monitor='val_loss', factor=0.0001, patience=5, min_delta=1E-4)

    # fit the keras model on the dataset
    history = model.fit(X_train, Y_train, epochs=50, batch_size=8, verbose=1,validation_split=0.2, callbacks=[rlrp])

    return model,history


#W2v Basic
modelDesc = 'NN Classifier Layers-Dropouts-Normalization-SGD'
dataDesc = 'W2V Basic Data'
model_fitted,history = modelNN_withDropoutsAndNormalization(X_train_w2v,y_train_w2v_cat)

Model: "sequential_3"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_9 (Dense)                      │ (None, 150)                 │          22,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout (Dropout)                    │ (None, 150)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization                  │ (None, 150)                 │             600 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_10 (Dense)                     │ (None, 100)                 │          15,100 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_1 (Dropout)                  │ (None, 100)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_1                │ (None, 100)                 │             400 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_11 (Dense)                     │ (None, 50)                  │           5,050 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_2 (Dropout)                  │ (None, 50)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_2                │ (None, 50)                  │             200 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_12 (Dense)                     │ (None, 5)                   │             255 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 44,405 (173.46 KB)

 Trainable params: 43,805 (171.11 KB)

 Non-trainable params: 600 (2.34 KB)

Epoch 1/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 3s 15ms/step - categorical_accuracy: 0.1803 - loss: 1.9881 - val_categorical_accuracy: 0.6418 - val_loss: 1.4404 - learning_rate: 0.0010
Epoch 2/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.3165 - loss: 1.6796 - val_categorical_accuracy: 0.7313 - val_loss: 1.2086 - learning_rate: 0.0010
Epoch 3/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.3698 - loss: 1.5810 - val_categorical_accuracy: 0.7015 - val_loss: 1.1435 - learning_rate: 0.0010
Epoch 4/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.4886 - loss: 1.4332 - val_categorical_accuracy: 0.7313 - val_loss: 1.0615 - learning_rate: 0.0010
Epoch 5/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.5260 - loss: 1.2835 - val_categorical_accuracy: 0.7164 - val_loss: 1.0241 - learning_rate: 0.0010
Epoch 6/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.5946 - loss: 1.2584 - val_categorical_accuracy: 0.7015 - val_loss: 1.0001 - learning_rate: 0.0010
Epoch 7/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.5800 - loss: 1.2927 - val_categorical_accuracy: 0.6866 - val_loss: 1.0247 - learning_rate: 0.0010
Epoch 8/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6338 - loss: 1.1810 - val_categorical_accuracy: 0.7313 - val_loss: 0.9776 - learning_rate: 0.0010
Epoch 9/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6816 - loss: 1.1601 - val_categorical_accuracy: 0.7313 - val_loss: 0.9693 - learning_rate: 0.0010
Epoch 10/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.6354 - loss: 1.1996 - val_categorical_accuracy: 0.7313 - val_loss: 0.9672 - learning_rate: 0.0010
Epoch 11/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.6228 - loss: 1.2769 - val_categorical_accuracy: 0.7463 - val_loss: 0.9451 - learning_rate: 0.0010
Epoch 12/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6515 - loss: 1.0907 - val_categorical_accuracy: 0.7313 - val_loss: 0.9514 - learning_rate: 0.0010
Epoch 13/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.6929 - loss: 1.1149 - val_categorical_accuracy: 0.7313 - val_loss: 0.9489 - learning_rate: 0.0010
Epoch 14/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - categorical_accuracy: 0.6843 - loss: 1.1730 - val_categorical_accuracy: 0.7313 - val_loss: 0.9858 - learning_rate: 0.0010
Epoch 15/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6997 - loss: 1.2041 - val_categorical_accuracy: 0.7313 - val_loss: 0.9538 - learning_rate: 0.0010
Epoch 16/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7118 - loss: 1.0896 - val_categorical_accuracy: 0.7313 - val_loss: 0.9618 - learning_rate: 0.0010
Epoch 17/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.6928 - loss: 1.0947 - val_categorical_accuracy: 0.7313 - val_loss: 0.9563 - learning_rate: 1.0000e-07
Epoch 18/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - categorical_accuracy: 0.6986 - loss: 1.1872 - val_categorical_accuracy: 0.7313 - val_loss: 0.9516 - learning_rate: 1.0000e-07
Epoch 19/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - categorical_accuracy: 0.7037 - loss: 1.1679 - val_categorical_accuracy: 0.7313 - val_loss: 0.9508 - learning_rate: 1.0000e-07
Epoch 20/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - categorical_accuracy: 0.6918 - loss: 1.0501 - val_categorical_accuracy: 0.7313 - val_loss: 0.9511 - learning_rate: 1.0000e-07
Epoch 21/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6860 - loss: 1.0767 - val_categorical_accuracy: 0.7313 - val_loss: 0.9527 - learning_rate: 1.0000e-07
Epoch 22/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7015 - loss: 1.1188 - val_categorical_accuracy: 0.7313 - val_loss: 0.9528 - learning_rate: 1.0000e-11
Epoch 23/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7108 - loss: 1.0660 - val_categorical_accuracy: 0.7313 - val_loss: 0.9549 - learning_rate: 1.0000e-11
Epoch 24/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6973 - loss: 1.1450 - val_categorical_accuracy: 0.7313 - val_loss: 0.9569 - learning_rate: 1.0000e-11
Epoch 25/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6877 - loss: 1.1309 - val_categorical_accuracy: 0.7313 - val_loss: 0.9537 - learning_rate: 1.0000e-11
Epoch 26/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6917 - loss: 1.0979 - val_categorical_accuracy: 0.7313 - val_loss: 0.9515 - learning_rate: 1.0000e-11
Epoch 27/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7097 - loss: 1.0279 - val_categorical_accuracy: 0.7313 - val_loss: 0.9530 - learning_rate: 1.0000e-15
Epoch 28/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7033 - loss: 1.1482 - val_categorical_accuracy: 0.7313 - val_loss: 0.9526 - learning_rate: 1.0000e-15
Epoch 29/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7084 - loss: 1.0869 - val_categorical_accuracy: 0.7313 - val_loss: 0.9527 - learning_rate: 1.0000e-15
Epoch 30/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - categorical_accuracy: 0.7101 - loss: 1.1142 - val_categorical_accuracy: 0.7313 - val_loss: 0.9520 - learning_rate: 1.0000e-15
Epoch 31/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7145 - loss: 0.9873 - val_categorical_accuracy: 0.7313 - val_loss: 0.9535 - learning_rate: 1.0000e-15
Epoch 32/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6921 - loss: 1.0973 - val_categorical_accuracy: 0.7313 - val_loss: 0.9519 - learning_rate: 1.0000e-19
Epoch 33/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7023 - loss: 1.0975 - val_categorical_accuracy: 0.7313 - val_loss: 0.9535 - learning_rate: 1.0000e-19
Epoch 34/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7041 - loss: 1.1458 - val_categorical_accuracy: 0.7313 - val_loss: 0.9550 - learning_rate: 1.0000e-19
Epoch 35/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6912 - loss: 1.1437 - val_categorical_accuracy: 0.7313 - val_loss: 0.9543 - learning_rate: 1.0000e-19
Epoch 36/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6715 - loss: 1.1084 - val_categorical_accuracy: 0.7313 - val_loss: 0.9536 - learning_rate: 1.0000e-19
Epoch 37/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.7023 - loss: 1.1548 - val_categorical_accuracy: 0.7313 - val_loss: 0.9529 - learning_rate: 1.0000e-23
Epoch 38/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - categorical_accuracy: 0.6977 - loss: 1.1456 - val_categorical_accuracy: 0.7313 - val_loss: 0.9534 - learning_rate: 1.0000e-23
Epoch 39/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.6784 - loss: 1.1498 - val_categorical_accuracy: 0.7313 - val_loss: 0.9539 - learning_rate: 1.0000e-23
Epoch 40/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - categorical_accuracy: 0.6703 - loss: 1.1567 - val_categorical_accuracy: 0.7313 - val_loss: 0.9517 - learning_rate: 1.0000e-23
Epoch 41/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7047 - loss: 1.1214 - val_categorical_accuracy: 0.7313 - val_loss: 0.9522 - learning_rate: 1.0000e-23
Epoch 42/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.6739 - loss: 1.1304 - val_categorical_accuracy: 0.7313 - val_loss: 0.9536 - learning_rate: 1.0000e-27
Epoch 43/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - categorical_accuracy: 0.6860 - loss: 1.1016 - val_categorical_accuracy: 0.7313 - val_loss: 0.9537 - learning_rate: 1.0000e-27
Epoch 44/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - categorical_accuracy: 0.6956 - loss: 1.0797 - val_categorical_accuracy: 0.7313 - val_loss: 0.9531 - learning_rate: 1.0000e-27
Epoch 45/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - categorical_accuracy: 0.6814 - loss: 1.1488 - val_categorical_accuracy: 0.7313 - val_loss: 0.9520 - learning_rate: 1.0000e-27
Epoch 46/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - categorical_accuracy: 0.6948 - loss: 1.1660 - val_categorical_accuracy: 0.7313 - val_loss: 0.9532 - learning_rate: 1.0000e-27
Epoch 47/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7047 - loss: 1.1619 - val_categorical_accuracy: 0.7313 - val_loss: 0.9543 - learning_rate: 1.0000e-31
Epoch 48/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6673 - loss: 1.1309 - val_categorical_accuracy: 0.7313 - val_loss: 0.9560 - learning_rate: 1.0000e-31
Epoch 49/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7009 - loss: 1.0466 - val_categorical_accuracy: 0.7313 - val_loss: 0.9569 - learning_rate: 1.0000e-31
Epoch 50/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6988 - loss: 1.0694 - val_categorical_accuracy: 0.7313 - val_loss: 0.9558 - learning_rate: 1.0000e-31


combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=model_fitted,historyNN=history,Accuracy='categorical_accuracy',ValidationAccuracy='val_categorical_accuracy',DataDesc=dataDesc,ModelDesc=modelDesc,X_train=X_train_w2v,Y_train=y_train_w2v_cat,X_test=X_test_w2v,Y_test=y_test_w2v_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 84ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :  Data Description                                Model Description  Train Accuracy  Test Accuracy  F1 Score
0   W2V Basic Data  NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69


Collating Results : 
-----------------------
   Data Description                                Model Description  Train Accuracy  Test Accuracy  F1 Score
0         W2V Basic                         Basic Model with 1 layer           78.44          75.00     65.16
0  Glove Basic Data                         Basic Model with 1 layer           91.02          73.81     69.07
0  TFIDF Basic Data                         Basic Model with 1 layer           94.01          70.24     61.33
0    W2V Basic Data  NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69


modelDesc = 'NN Classifier Layers-Dropouts-Normalization-SGD'
#Glove Basic
dataDesc = 'Glove Basic data'
model_fitted,history = modelNN_withDropoutsAndNormalization(X_train_glove,y_train_glove_cat)
combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=model_fitted,historyNN=history,Accuracy='categorical_accuracy',ValidationAccuracy='val_categorical_accuracy',DataDesc=dataDesc,ModelDesc=modelDesc,X_train=X_train_glove,Y_train=y_train_glove_cat,X_test=X_test_glove,Y_test=y_test_glove_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Model: "sequential_4"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_13 (Dense)                     │ (None, 150)                 │          22,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_3 (Dropout)                  │ (None, 150)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_3                │ (None, 150)                 │             600 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_14 (Dense)                     │ (None, 100)                 │          15,100 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_4 (Dropout)                  │ (None, 100)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_4                │ (None, 100)                 │             400 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_15 (Dense)                     │ (None, 50)                  │           5,050 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_5 (Dropout)                  │ (None, 50)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_5                │ (None, 50)                  │             200 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_16 (Dense)                     │ (None, 5)                   │             255 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 44,405 (173.46 KB)

 Trainable params: 43,805 (171.11 KB)

 Non-trainable params: 600 (2.34 KB)

Epoch 1/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 3s 15ms/step - categorical_accuracy: 0.1909 - loss: 2.0333 - val_categorical_accuracy: 0.4627 - val_loss: 1.5163 - learning_rate: 0.0010
Epoch 2/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.2504 - loss: 1.7948 - val_categorical_accuracy: 0.6418 - val_loss: 1.4046 - learning_rate: 0.0010
Epoch 3/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.3514 - loss: 1.5916 - val_categorical_accuracy: 0.6866 - val_loss: 1.3445 - learning_rate: 0.0010
Epoch 4/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.4380 - loss: 1.3624 - val_categorical_accuracy: 0.6866 - val_loss: 1.2683 - learning_rate: 0.0010
Epoch 5/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.5118 - loss: 1.2923 - val_categorical_accuracy: 0.7015 - val_loss: 1.1722 - learning_rate: 0.0010
Epoch 6/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.5629 - loss: 1.2740 - val_categorical_accuracy: 0.7313 - val_loss: 1.1213 - learning_rate: 0.0010
Epoch 7/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6024 - loss: 1.2321 - val_categorical_accuracy: 0.7313 - val_loss: 1.0891 - learning_rate: 0.0010
Epoch 8/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - categorical_accuracy: 0.6552 - loss: 1.1636 - val_categorical_accuracy: 0.7313 - val_loss: 1.0347 - learning_rate: 0.0010
Epoch 9/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.6303 - loss: 1.2265 - val_categorical_accuracy: 0.7313 - val_loss: 1.0304 - learning_rate: 0.0010
Epoch 10/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.6778 - loss: 1.1386 - val_categorical_accuracy: 0.7313 - val_loss: 0.9999 - learning_rate: 0.0010
Epoch 11/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6832 - loss: 1.2405 - val_categorical_accuracy: 0.7313 - val_loss: 1.0034 - learning_rate: 0.0010
Epoch 12/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6530 - loss: 1.1589 - val_categorical_accuracy: 0.7313 - val_loss: 0.9791 - learning_rate: 0.0010
Epoch 13/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6833 - loss: 1.1108 - val_categorical_accuracy: 0.7313 - val_loss: 0.9818 - learning_rate: 0.0010
Epoch 14/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6815 - loss: 1.1767 - val_categorical_accuracy: 0.7313 - val_loss: 0.9775 - learning_rate: 0.0010
Epoch 15/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6989 - loss: 1.1953 - val_categorical_accuracy: 0.7313 - val_loss: 0.9747 - learning_rate: 0.0010
Epoch 16/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7016 - loss: 1.1150 - val_categorical_accuracy: 0.7313 - val_loss: 0.9896 - learning_rate: 0.0010
Epoch 17/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6753 - loss: 1.1673 - val_categorical_accuracy: 0.7313 - val_loss: 0.9915 - learning_rate: 0.0010
Epoch 18/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7098 - loss: 1.0525 - val_categorical_accuracy: 0.7313 - val_loss: 0.9947 - learning_rate: 0.0010
Epoch 19/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7154 - loss: 1.1063 - val_categorical_accuracy: 0.7313 - val_loss: 0.9997 - learning_rate: 0.0010
Epoch 20/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7154 - loss: 1.0934 - val_categorical_accuracy: 0.7313 - val_loss: 1.0006 - learning_rate: 0.0010
Epoch 21/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7071 - loss: 1.1344 - val_categorical_accuracy: 0.7313 - val_loss: 0.9889 - learning_rate: 1.0000e-07
Epoch 22/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7014 - loss: 1.0867 - val_categorical_accuracy: 0.7313 - val_loss: 0.9872 - learning_rate: 1.0000e-07
Epoch 23/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6983 - loss: 1.0313 - val_categorical_accuracy: 0.7313 - val_loss: 0.9866 - learning_rate: 1.0000e-07
Epoch 24/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7089 - loss: 1.0884 - val_categorical_accuracy: 0.7313 - val_loss: 0.9872 - learning_rate: 1.0000e-07
Epoch 25/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7128 - loss: 1.1152 - val_categorical_accuracy: 0.7313 - val_loss: 0.9828 - learning_rate: 1.0000e-07
Epoch 26/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7154 - loss: 1.0881 - val_categorical_accuracy: 0.7313 - val_loss: 0.9839 - learning_rate: 1.0000e-11
Epoch 27/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7125 - loss: 1.1634 - val_categorical_accuracy: 0.7313 - val_loss: 0.9820 - learning_rate: 1.0000e-11
Epoch 28/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - categorical_accuracy: 0.7135 - loss: 1.1281 - val_categorical_accuracy: 0.7313 - val_loss: 0.9852 - learning_rate: 1.0000e-11
Epoch 29/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - categorical_accuracy: 0.7138 - loss: 1.1416 - val_categorical_accuracy: 0.7313 - val_loss: 0.9863 - learning_rate: 1.0000e-11
Epoch 30/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.7035 - loss: 1.1379 - val_categorical_accuracy: 0.7313 - val_loss: 0.9857 - learning_rate: 1.0000e-11
Epoch 31/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - categorical_accuracy: 0.7062 - loss: 1.0428 - val_categorical_accuracy: 0.7313 - val_loss: 0.9864 - learning_rate: 1.0000e-15
Epoch 32/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7125 - loss: 1.0817 - val_categorical_accuracy: 0.7313 - val_loss: 0.9877 - learning_rate: 1.0000e-15
Epoch 33/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7015 - loss: 1.1062 - val_categorical_accuracy: 0.7313 - val_loss: 0.9853 - learning_rate: 1.0000e-15
Epoch 34/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - categorical_accuracy: 0.7207 - loss: 1.1235 - val_categorical_accuracy: 0.7313 - val_loss: 0.9854 - learning_rate: 1.0000e-15
Epoch 35/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - categorical_accuracy: 0.6992 - loss: 1.0917 - val_categorical_accuracy: 0.7313 - val_loss: 0.9841 - learning_rate: 1.0000e-15
Epoch 36/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - categorical_accuracy: 0.7102 - loss: 1.0577 - val_categorical_accuracy: 0.7313 - val_loss: 0.9855 - learning_rate: 1.0000e-19
Epoch 37/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.7058 - loss: 1.1388 - val_categorical_accuracy: 0.7313 - val_loss: 0.9853 - learning_rate: 1.0000e-19
Epoch 38/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - categorical_accuracy: 0.7110 - loss: 1.0880 - val_categorical_accuracy: 0.7313 - val_loss: 0.9839 - learning_rate: 1.0000e-19
Epoch 39/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6763 - loss: 1.1493 - val_categorical_accuracy: 0.7313 - val_loss: 0.9832 - learning_rate: 1.0000e-19
Epoch 40/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7070 - loss: 1.1040 - val_categorical_accuracy: 0.7313 - val_loss: 0.9833 - learning_rate: 1.0000e-19
Epoch 41/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7111 - loss: 1.0724 - val_categorical_accuracy: 0.7313 - val_loss: 0.9838 - learning_rate: 1.0000e-23
Epoch 42/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7308 - loss: 1.0573 - val_categorical_accuracy: 0.7313 - val_loss: 0.9849 - learning_rate: 1.0000e-23
Epoch 43/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6937 - loss: 1.1041 - val_categorical_accuracy: 0.7313 - val_loss: 0.9870 - learning_rate: 1.0000e-23
Epoch 44/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7035 - loss: 1.1018 - val_categorical_accuracy: 0.7313 - val_loss: 0.9888 - learning_rate: 1.0000e-23
Epoch 45/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6879 - loss: 1.1311 - val_categorical_accuracy: 0.7313 - val_loss: 0.9886 - learning_rate: 1.0000e-23
Epoch 46/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7091 - loss: 1.1114 - val_categorical_accuracy: 0.7313 - val_loss: 0.9898 - learning_rate: 1.0000e-27
Epoch 47/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7152 - loss: 1.1266 - val_categorical_accuracy: 0.7313 - val_loss: 0.9873 - learning_rate: 1.0000e-27
Epoch 48/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7001 - loss: 1.0566 - val_categorical_accuracy: 0.7313 - val_loss: 0.9842 - learning_rate: 1.0000e-27
Epoch 49/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6947 - loss: 1.0976 - val_categorical_accuracy: 0.7313 - val_loss: 0.9830 - learning_rate: 1.0000e-27
Epoch 50/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7069 - loss: 1.1180 - val_categorical_accuracy: 0.7313 - val_loss: 0.9848 - learning_rate: 1.0000e-27
Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 59ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :   Data Description                                Model Description  Train Accuracy  Test Accuracy  F1 Score
0  Glove Basic data  NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69


Collating Results : 
-----------------------
   Data Description                                Model Description  Train Accuracy  Test Accuracy  F1 Score
0         W2V Basic                         Basic Model with 1 layer           78.44          75.00     65.16
0  Glove Basic Data                         Basic Model with 1 layer           91.02          73.81     69.07
0  TFIDF Basic Data                         Basic Model with 1 layer           94.01          70.24     61.33
0    W2V Basic Data  NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  Glove Basic data  NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69


modelDesc = 'NN Classifier Layers-Dropouts-Normalization-SGD'
#TFIDF Basic Data
dataDesc = 'TFIDF Basic data'
model_fitted,history = modelNN_withDropoutsAndNormalization(X_train_tfidf,y_train_tfidf_cat)
combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=model_fitted,historyNN=history,Accuracy='categorical_accuracy',ValidationAccuracy='val_categorical_accuracy',DataDesc=dataDesc,ModelDesc=modelDesc,X_train=X_train_tfidf,Y_train=y_train_tfidf_cat,X_test=X_test_tfidf,Y_test=y_test_tfidf_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Model: "sequential_5"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_17 (Dense)                     │ (None, 150)                 │         425,850 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_6 (Dropout)                  │ (None, 150)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_6                │ (None, 150)                 │             600 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_18 (Dense)                     │ (None, 100)                 │          15,100 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_7 (Dropout)                  │ (None, 100)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_7                │ (None, 100)                 │             400 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_19 (Dense)                     │ (None, 50)                  │           5,050 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_8 (Dropout)                  │ (None, 50)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_8                │ (None, 50)                  │             200 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_20 (Dense)                     │ (None, 5)                   │             255 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 447,455 (1.71 MB)

 Trainable params: 446,855 (1.70 MB)

 Non-trainable params: 600 (2.34 KB)

Epoch 1/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 3s 23ms/step - categorical_accuracy: 0.2172 - loss: 2.0094 - val_categorical_accuracy: 0.4925 - val_loss: 1.4435 - learning_rate: 0.0010
Epoch 2/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - categorical_accuracy: 0.2398 - loss: 1.7997 - val_categorical_accuracy: 0.7015 - val_loss: 1.2823 - learning_rate: 0.0010
Epoch 3/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - categorical_accuracy: 0.3373 - loss: 1.6923 - val_categorical_accuracy: 0.7313 - val_loss: 1.1436 - learning_rate: 0.0010
Epoch 4/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - categorical_accuracy: 0.4669 - loss: 1.4553 - val_categorical_accuracy: 0.7313 - val_loss: 1.1325 - learning_rate: 0.0010
Epoch 5/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - categorical_accuracy: 0.4581 - loss: 1.4263 - val_categorical_accuracy: 0.7313 - val_loss: 1.0347 - learning_rate: 0.0010
Epoch 6/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.5662 - loss: 1.3056 - val_categorical_accuracy: 0.7313 - val_loss: 1.0203 - learning_rate: 0.0010
Epoch 7/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - categorical_accuracy: 0.5954 - loss: 1.2243 - val_categorical_accuracy: 0.7313 - val_loss: 1.0078 - learning_rate: 0.0010
Epoch 8/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - categorical_accuracy: 0.6096 - loss: 1.2023 - val_categorical_accuracy: 0.7313 - val_loss: 0.9859 - learning_rate: 0.0010
Epoch 9/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - categorical_accuracy: 0.6471 - loss: 1.1375 - val_categorical_accuracy: 0.7313 - val_loss: 0.9740 - learning_rate: 0.0010
Epoch 10/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - categorical_accuracy: 0.6735 - loss: 1.1138 - val_categorical_accuracy: 0.7313 - val_loss: 0.9823 - learning_rate: 0.0010
Epoch 11/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - categorical_accuracy: 0.6556 - loss: 1.1297 - val_categorical_accuracy: 0.7313 - val_loss: 0.9852 - learning_rate: 0.0010
Epoch 12/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - categorical_accuracy: 0.6454 - loss: 1.1833 - val_categorical_accuracy: 0.7313 - val_loss: 0.9952 - learning_rate: 0.0010
Epoch 13/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - categorical_accuracy: 0.6674 - loss: 1.1736 - val_categorical_accuracy: 0.7313 - val_loss: 1.0090 - learning_rate: 0.0010
Epoch 14/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - categorical_accuracy: 0.6998 - loss: 1.1584 - val_categorical_accuracy: 0.7313 - val_loss: 0.9969 - learning_rate: 0.0010
Epoch 15/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - categorical_accuracy: 0.6884 - loss: 1.1024 - val_categorical_accuracy: 0.7313 - val_loss: 0.9975 - learning_rate: 1.0000e-07
Epoch 16/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - categorical_accuracy: 0.7026 - loss: 1.0633 - val_categorical_accuracy: 0.7313 - val_loss: 0.9936 - learning_rate: 1.0000e-07
Epoch 17/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - categorical_accuracy: 0.7042 - loss: 1.1004 - val_categorical_accuracy: 0.7313 - val_loss: 0.9919 - learning_rate: 1.0000e-07
Epoch 18/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - categorical_accuracy: 0.6881 - loss: 1.1184 - val_categorical_accuracy: 0.7313 - val_loss: 0.9919 - learning_rate: 1.0000e-07
Epoch 19/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - categorical_accuracy: 0.6855 - loss: 1.1182 - val_categorical_accuracy: 0.7313 - val_loss: 0.9923 - learning_rate: 1.0000e-07
Epoch 20/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - categorical_accuracy: 0.6660 - loss: 1.1489 - val_categorical_accuracy: 0.7313 - val_loss: 0.9905 - learning_rate: 1.0000e-11
Epoch 21/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - categorical_accuracy: 0.6827 - loss: 1.1796 - val_categorical_accuracy: 0.7313 - val_loss: 0.9915 - learning_rate: 1.0000e-11
Epoch 22/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - categorical_accuracy: 0.6819 - loss: 1.1111 - val_categorical_accuracy: 0.7313 - val_loss: 0.9938 - learning_rate: 1.0000e-11
Epoch 23/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - categorical_accuracy: 0.6934 - loss: 1.0842 - val_categorical_accuracy: 0.7313 - val_loss: 0.9945 - learning_rate: 1.0000e-11
Epoch 24/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - categorical_accuracy: 0.6828 - loss: 1.1051 - val_categorical_accuracy: 0.7313 - val_loss: 0.9950 - learning_rate: 1.0000e-11
Epoch 25/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - categorical_accuracy: 0.6854 - loss: 1.1393 - val_categorical_accuracy: 0.7313 - val_loss: 0.9958 - learning_rate: 1.0000e-15
Epoch 26/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - categorical_accuracy: 0.7021 - loss: 1.0492 - val_categorical_accuracy: 0.7313 - val_loss: 0.9965 - learning_rate: 1.0000e-15
Epoch 27/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - categorical_accuracy: 0.6945 - loss: 1.1304 - val_categorical_accuracy: 0.7313 - val_loss: 0.9986 - learning_rate: 1.0000e-15
Epoch 28/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - categorical_accuracy: 0.7184 - loss: 1.0360 - val_categorical_accuracy: 0.7313 - val_loss: 0.9953 - learning_rate: 1.0000e-15
Epoch 29/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - categorical_accuracy: 0.6636 - loss: 1.1736 - val_categorical_accuracy: 0.7313 - val_loss: 0.9935 - learning_rate: 1.0000e-15
Epoch 30/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - categorical_accuracy: 0.6781 - loss: 1.0821 - val_categorical_accuracy: 0.7313 - val_loss: 0.9943 - learning_rate: 1.0000e-19
Epoch 31/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - categorical_accuracy: 0.6853 - loss: 1.1893 - val_categorical_accuracy: 0.7313 - val_loss: 0.9955 - learning_rate: 1.0000e-19
Epoch 32/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - categorical_accuracy: 0.7017 - loss: 1.1868 - val_categorical_accuracy: 0.7313 - val_loss: 0.9969 - learning_rate: 1.0000e-19
Epoch 33/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - categorical_accuracy: 0.6659 - loss: 1.1229 - val_categorical_accuracy: 0.7313 - val_loss: 1.0005 - learning_rate: 1.0000e-19
Epoch 34/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 30ms/step - categorical_accuracy: 0.7032 - loss: 1.1082 - val_categorical_accuracy: 0.7313 - val_loss: 0.9969 - learning_rate: 1.0000e-19
Epoch 35/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step - categorical_accuracy: 0.6819 - loss: 1.1286 - val_categorical_accuracy: 0.7313 - val_loss: 0.9966 - learning_rate: 1.0000e-23
Epoch 36/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - categorical_accuracy: 0.6910 - loss: 1.1206 - val_categorical_accuracy: 0.7313 - val_loss: 0.9949 - learning_rate: 1.0000e-23
Epoch 37/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step - categorical_accuracy: 0.6726 - loss: 1.0237 - val_categorical_accuracy: 0.7313 - val_loss: 0.9947 - learning_rate: 1.0000e-23
Epoch 38/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - categorical_accuracy: 0.6701 - loss: 1.1452 - val_categorical_accuracy: 0.7313 - val_loss: 0.9948 - learning_rate: 1.0000e-23
Epoch 39/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - categorical_accuracy: 0.6948 - loss: 1.0696 - val_categorical_accuracy: 0.7313 - val_loss: 0.9974 - learning_rate: 1.0000e-23
Epoch 40/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - categorical_accuracy: 0.6912 - loss: 1.1512 - val_categorical_accuracy: 0.7313 - val_loss: 0.9980 - learning_rate: 1.0000e-27
Epoch 41/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - categorical_accuracy: 0.6790 - loss: 1.1467 - val_categorical_accuracy: 0.7313 - val_loss: 0.9976 - learning_rate: 1.0000e-27
Epoch 42/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - categorical_accuracy: 0.6848 - loss: 1.1283 - val_categorical_accuracy: 0.7313 - val_loss: 0.9989 - learning_rate: 1.0000e-27
Epoch 43/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - categorical_accuracy: 0.7016 - loss: 1.0960 - val_categorical_accuracy: 0.7313 - val_loss: 0.9968 - learning_rate: 1.0000e-27
Epoch 44/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - categorical_accuracy: 0.6897 - loss: 1.1301 - val_categorical_accuracy: 0.7313 - val_loss: 0.9956 - learning_rate: 1.0000e-27
Epoch 45/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - categorical_accuracy: 0.7065 - loss: 1.1565 - val_categorical_accuracy: 0.7313 - val_loss: 0.9968 - learning_rate: 1.0000e-31
Epoch 46/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - categorical_accuracy: 0.7018 - loss: 1.1953 - val_categorical_accuracy: 0.7313 - val_loss: 0.9979 - learning_rate: 1.0000e-31
Epoch 47/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - categorical_accuracy: 0.6946 - loss: 1.0913 - val_categorical_accuracy: 0.7313 - val_loss: 1.0015 - learning_rate: 1.0000e-31
Epoch 48/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step - categorical_accuracy: 0.6728 - loss: 1.1811 - val_categorical_accuracy: 0.7313 - val_loss: 0.9995 - learning_rate: 1.0000e-31
Epoch 49/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 31ms/step - categorical_accuracy: 0.6837 - loss: 1.1139 - val_categorical_accuracy: 0.7313 - val_loss: 0.9992 - learning_rate: 1.0000e-31
Epoch 50/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - categorical_accuracy: 0.6656 - loss: 1.1557 - val_categorical_accuracy: 0.7313 - val_loss: 0.9994 - learning_rate: 1.0000e-35
Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 1s 167ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :   Data Description                                Model Description  Train Accuracy  Test Accuracy  F1 Score
0  TFIDF Basic data  NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69


Collating Results : 
-----------------------
   Data Description                                Model Description  Train Accuracy  Test Accuracy  F1 Score
0         W2V Basic                         Basic Model with 1 layer           78.44          75.00     65.16
0  Glove Basic Data                         Basic Model with 1 layer           91.02          73.81     69.07
0  TFIDF Basic Data                         Basic Model with 1 layer           94.01          70.24     61.33
0    W2V Basic Data  NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  Glove Basic data  NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  TFIDF Basic data  NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69


def modelNN_withDropoutsAndNormalization_Adam(X_train,Y_train):
    param = 1e-4

    # define the model
    model = Sequential()

    model.add(Dense(150, input_dim=X_train.shape[1], activation='relu', kernel_initializer='he_uniform', kernel_regularizer=l2(param),
                    kernel_constraint=unit_norm()))
    model.add(Dropout(0.2))
    model.add(BatchNormalization())
    model.add(Dense(100, activation='relu', kernel_initializer='he_uniform', kernel_regularizer=l2(param),
                    kernel_constraint=unit_norm()))
    model.add(Dropout(0.5))
    model.add(BatchNormalization())
    model.add(Dense(50, activation='relu', kernel_initializer='he_uniform', kernel_regularizer=l2(param),
                    kernel_constraint=unit_norm()))
    model.add(Dropout(0.5))
    model.add(BatchNormalization())
    model.add(Dense(5, activation='softmax', kernel_regularizer=l2(param),
                    kernel_constraint=unit_norm()))
    model.summary()

    model.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(learning_rate=1e-3), metrics=['categorical_accuracy'])
    # Use earlystopping
    callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=7, min_delta=1E-3)
    rlrp = ReduceLROnPlateau(monitor='val_loss', factor=0.0001, patience=5, min_delta=1E-4)

    # fit the keras model on the dataset
    history = model.fit(X_train, Y_train, epochs=50, batch_size=8, verbose=1,validation_split=0.2, callbacks=[rlrp])

    return model,history


#W2v Basic
modelDesc = 'NN Classifier Layers-Dropouts-Normalization-ADAM'
dataDesc = 'W2V Basic Data'
model_lifted,history = modelNN_withDropoutsAndNormalization_Adam(X_train_w2v,y_train_w2v_cat)
combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=model_lifted,historyNN=history,Accuracy='categorical_accuracy',ValidationAccuracy='val_categorical_accuracy',DataDesc=dataDesc,ModelDesc=modelDesc,X_train=X_train_w2v,Y_train=y_train_w2v_cat,X_test=X_test_w2v,Y_test=y_test_w2v_cat)],ignore_index=False,sort=False)

Model: "sequential_6"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_21 (Dense)                     │ (None, 150)                 │          22,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_9 (Dropout)                  │ (None, 150)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_9                │ (None, 150)                 │             600 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_22 (Dense)                     │ (None, 100)                 │          15,100 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_10 (Dropout)                 │ (None, 100)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_10               │ (None, 100)                 │             400 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_23 (Dense)                     │ (None, 50)                  │           5,050 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_11 (Dropout)                 │ (None, 50)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_11               │ (None, 50)                  │             200 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_24 (Dense)                     │ (None, 5)                   │             255 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 44,405 (173.46 KB)

 Trainable params: 43,805 (171.11 KB)

 Non-trainable params: 600 (2.34 KB)

Epoch 1/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 9s 65ms/step - categorical_accuracy: 0.2067 - loss: 2.0296 - val_categorical_accuracy: 0.6716 - val_loss: 1.4958 - learning_rate: 0.0010
Epoch 2/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - categorical_accuracy: 0.2379 - loss: 1.8325 - val_categorical_accuracy: 0.7313 - val_loss: 1.3602 - learning_rate: 0.0010
Epoch 3/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - categorical_accuracy: 0.3123 - loss: 1.7151 - val_categorical_accuracy: 0.7313 - val_loss: 1.2443 - learning_rate: 0.0010
Epoch 4/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - categorical_accuracy: 0.3853 - loss: 1.5867 - val_categorical_accuracy: 0.7313 - val_loss: 1.1708 - learning_rate: 0.0010
Epoch 5/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - categorical_accuracy: 0.4512 - loss: 1.4179 - val_categorical_accuracy: 0.7313 - val_loss: 1.1170 - learning_rate: 0.0010
Epoch 6/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - categorical_accuracy: 0.4778 - loss: 1.4202 - val_categorical_accuracy: 0.7313 - val_loss: 1.0661 - learning_rate: 0.0010
Epoch 7/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - categorical_accuracy: 0.5802 - loss: 1.2701 - val_categorical_accuracy: 0.7313 - val_loss: 1.0101 - learning_rate: 0.0010
Epoch 8/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - categorical_accuracy: 0.5764 - loss: 1.2452 - val_categorical_accuracy: 0.7164 - val_loss: 1.0043 - learning_rate: 0.0010
Epoch 9/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.5984 - loss: 1.2416 - val_categorical_accuracy: 0.7313 - val_loss: 0.9970 - learning_rate: 0.0010
Epoch 10/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.5990 - loss: 1.1982 - val_categorical_accuracy: 0.7164 - val_loss: 0.9672 - learning_rate: 0.0010
Epoch 11/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6507 - loss: 1.1581 - val_categorical_accuracy: 0.7164 - val_loss: 0.9662 - learning_rate: 0.0010
Epoch 12/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6555 - loss: 1.1286 - val_categorical_accuracy: 0.7164 - val_loss: 0.9676 - learning_rate: 0.0010
Epoch 13/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6811 - loss: 1.0694 - val_categorical_accuracy: 0.7164 - val_loss: 0.9962 - learning_rate: 0.0010
Epoch 14/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6635 - loss: 1.1092 - val_categorical_accuracy: 0.7164 - val_loss: 0.9923 - learning_rate: 0.0010
Epoch 15/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6848 - loss: 1.1348 - val_categorical_accuracy: 0.7164 - val_loss: 0.9969 - learning_rate: 0.0010
Epoch 16/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6794 - loss: 1.0474 - val_categorical_accuracy: 0.7164 - val_loss: 0.9869 - learning_rate: 0.0010
Epoch 17/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6795 - loss: 1.2111 - val_categorical_accuracy: 0.7164 - val_loss: 0.9837 - learning_rate: 1.0000e-07
Epoch 18/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6852 - loss: 1.1002 - val_categorical_accuracy: 0.7164 - val_loss: 0.9770 - learning_rate: 1.0000e-07
Epoch 19/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6654 - loss: 1.0953 - val_categorical_accuracy: 0.7164 - val_loss: 0.9736 - learning_rate: 1.0000e-07
Epoch 20/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6954 - loss: 1.0955 - val_categorical_accuracy: 0.7164 - val_loss: 0.9753 - learning_rate: 1.0000e-07
Epoch 21/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6967 - loss: 1.0865 - val_categorical_accuracy: 0.7164 - val_loss: 0.9754 - learning_rate: 1.0000e-07
Epoch 22/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6750 - loss: 1.0431 - val_categorical_accuracy: 0.7164 - val_loss: 0.9752 - learning_rate: 1.0000e-11
Epoch 23/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6852 - loss: 0.9495 - val_categorical_accuracy: 0.7164 - val_loss: 0.9774 - learning_rate: 1.0000e-11
Epoch 24/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6884 - loss: 1.1188 - val_categorical_accuracy: 0.7164 - val_loss: 0.9807 - learning_rate: 1.0000e-11
Epoch 25/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7055 - loss: 1.0591 - val_categorical_accuracy: 0.7164 - val_loss: 0.9767 - learning_rate: 1.0000e-11
Epoch 26/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6707 - loss: 1.0925 - val_categorical_accuracy: 0.7164 - val_loss: 0.9760 - learning_rate: 1.0000e-11
Epoch 27/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6922 - loss: 1.0751 - val_categorical_accuracy: 0.7164 - val_loss: 0.9704 - learning_rate: 1.0000e-15
Epoch 28/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6684 - loss: 1.1393 - val_categorical_accuracy: 0.7164 - val_loss: 0.9727 - learning_rate: 1.0000e-15
Epoch 29/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6615 - loss: 1.0765 - val_categorical_accuracy: 0.7164 - val_loss: 0.9724 - learning_rate: 1.0000e-15
Epoch 30/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7088 - loss: 1.1823 - val_categorical_accuracy: 0.7164 - val_loss: 0.9698 - learning_rate: 1.0000e-15
Epoch 31/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6709 - loss: 1.1235 - val_categorical_accuracy: 0.7164 - val_loss: 0.9695 - learning_rate: 1.0000e-15
Epoch 32/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6808 - loss: 1.0750 - val_categorical_accuracy: 0.7164 - val_loss: 0.9694 - learning_rate: 1.0000e-19
Epoch 33/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6808 - loss: 1.0826 - val_categorical_accuracy: 0.7164 - val_loss: 0.9697 - learning_rate: 1.0000e-19
Epoch 34/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.6793 - loss: 1.1276 - val_categorical_accuracy: 0.7164 - val_loss: 0.9672 - learning_rate: 1.0000e-19
Epoch 35/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7110 - loss: 1.0483 - val_categorical_accuracy: 0.7164 - val_loss: 0.9699 - learning_rate: 1.0000e-19
Epoch 36/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.6673 - loss: 1.1077 - val_categorical_accuracy: 0.7164 - val_loss: 0.9701 - learning_rate: 1.0000e-19
Epoch 37/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.6949 - loss: 1.1042 - val_categorical_accuracy: 0.7164 - val_loss: 0.9710 - learning_rate: 1.0000e-23
Epoch 38/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6675 - loss: 1.1117 - val_categorical_accuracy: 0.7164 - val_loss: 0.9737 - learning_rate: 1.0000e-23
Epoch 39/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.6799 - loss: 1.0427 - val_categorical_accuracy: 0.7164 - val_loss: 0.9781 - learning_rate: 1.0000e-23
Epoch 40/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - categorical_accuracy: 0.6829 - loss: 1.0815 - val_categorical_accuracy: 0.7164 - val_loss: 0.9771 - learning_rate: 1.0000e-23
Epoch 41/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - categorical_accuracy: 0.6797 - loss: 1.0504 - val_categorical_accuracy: 0.7164 - val_loss: 0.9731 - learning_rate: 1.0000e-23
Epoch 42/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - categorical_accuracy: 0.6978 - loss: 1.1011 - val_categorical_accuracy: 0.7164 - val_loss: 0.9728 - learning_rate: 1.0000e-27
Epoch 43/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - categorical_accuracy: 0.6994 - loss: 1.1671 - val_categorical_accuracy: 0.7164 - val_loss: 0.9673 - learning_rate: 1.0000e-27
Epoch 44/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - categorical_accuracy: 0.7372 - loss: 0.9847 - val_categorical_accuracy: 0.7164 - val_loss: 0.9717 - learning_rate: 1.0000e-27
Epoch 45/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - categorical_accuracy: 0.7002 - loss: 1.0916 - val_categorical_accuracy: 0.7164 - val_loss: 0.9745 - learning_rate: 1.0000e-27
Epoch 46/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - categorical_accuracy: 0.7010 - loss: 1.1188 - val_categorical_accuracy: 0.7164 - val_loss: 0.9726 - learning_rate: 1.0000e-27
Epoch 47/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - categorical_accuracy: 0.7129 - loss: 1.0534 - val_categorical_accuracy: 0.7164 - val_loss: 0.9711 - learning_rate: 1.0000e-31
Epoch 48/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - categorical_accuracy: 0.6924 - loss: 1.0643 - val_categorical_accuracy: 0.7164 - val_loss: 0.9709 - learning_rate: 1.0000e-31
Epoch 49/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - categorical_accuracy: 0.7031 - loss: 1.0664 - val_categorical_accuracy: 0.7164 - val_loss: 0.9679 - learning_rate: 1.0000e-31
Epoch 50/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7092 - loss: 1.0628 - val_categorical_accuracy: 0.7164 - val_loss: 0.9706 - learning_rate: 1.0000e-31
Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 58ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      0.97      0.84        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.71        84
   macro avg       0.15      0.19      0.17        84
weighted avg       0.55      0.71      0.62        84

Result Summary 
 --------------------- 
 :  Data Description                                 Model Description  Train Accuracy  Test Accuracy  F1 Score
0   W2V Basic Data  NN Classifier Layers-Dropouts-Normalization-ADAM           73.95          71.43     61.94


#Glove Basic
modelDesc = 'NN Classifier Layers-Dropouts-Normalization-ADAM'
dataDesc = 'Glove Basic Data'
model_fitted,history = modelNN_withDropoutsAndNormalization_Adam(X_train_glove,y_train_glove_cat)
combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=model_fitted,historyNN=history,Accuracy='categorical_accuracy',ValidationAccuracy='val_categorical_accuracy',DataDesc=dataDesc,ModelDesc=modelDesc,X_train=X_train_glove,Y_train=y_train_glove_cat,X_test=X_test_glove,Y_test=y_test_glove_cat)],ignore_index=False,sort=False)

Model: "sequential_7"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_25 (Dense)                     │ (None, 150)                 │          22,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_12 (Dropout)                 │ (None, 150)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_12               │ (None, 150)                 │             600 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_26 (Dense)                     │ (None, 100)                 │          15,100 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_13 (Dropout)                 │ (None, 100)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_13               │ (None, 100)                 │             400 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_27 (Dense)                     │ (None, 50)                  │           5,050 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_14 (Dropout)                 │ (None, 50)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_14               │ (None, 50)                  │             200 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_28 (Dense)                     │ (None, 5)                   │             255 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 44,405 (173.46 KB)

 Trainable params: 43,805 (171.11 KB)

 Non-trainable params: 600 (2.34 KB)

Epoch 1/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 5s 16ms/step - categorical_accuracy: 0.2111 - loss: 1.9025 - val_categorical_accuracy: 0.6716 - val_loss: 1.5032 - learning_rate: 0.0010
Epoch 2/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.2062 - loss: 1.7690 - val_categorical_accuracy: 0.6567 - val_loss: 1.4370 - learning_rate: 0.0010
Epoch 3/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.3204 - loss: 1.6699 - val_categorical_accuracy: 0.7164 - val_loss: 1.3980 - learning_rate: 0.0010
Epoch 4/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.3601 - loss: 1.5007 - val_categorical_accuracy: 0.7015 - val_loss: 1.3459 - learning_rate: 0.0010
Epoch 5/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.4561 - loss: 1.4503 - val_categorical_accuracy: 0.7015 - val_loss: 1.2862 - learning_rate: 0.0010
Epoch 6/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.4757 - loss: 1.4613 - val_categorical_accuracy: 0.7313 - val_loss: 1.2189 - learning_rate: 0.0010
Epoch 7/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.5730 - loss: 1.2599 - val_categorical_accuracy: 0.7313 - val_loss: 1.1872 - learning_rate: 0.0010
Epoch 8/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.5936 - loss: 1.2282 - val_categorical_accuracy: 0.7313 - val_loss: 1.1317 - learning_rate: 0.0010
Epoch 9/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6402 - loss: 1.2261 - val_categorical_accuracy: 0.7313 - val_loss: 1.0780 - learning_rate: 0.0010
Epoch 10/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6230 - loss: 1.1372 - val_categorical_accuracy: 0.7313 - val_loss: 1.0385 - learning_rate: 0.0010
Epoch 11/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - categorical_accuracy: 0.6845 - loss: 1.1242 - val_categorical_accuracy: 0.7313 - val_loss: 0.9928 - learning_rate: 0.0010
Epoch 12/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.6640 - loss: 1.0587 - val_categorical_accuracy: 0.7313 - val_loss: 0.9666 - learning_rate: 0.0010
Epoch 13/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - categorical_accuracy: 0.6725 - loss: 1.1318 - val_categorical_accuracy: 0.7313 - val_loss: 0.9557 - learning_rate: 0.0010
Epoch 14/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - categorical_accuracy: 0.7127 - loss: 1.1237 - val_categorical_accuracy: 0.7313 - val_loss: 0.9360 - learning_rate: 0.0010
Epoch 15/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - categorical_accuracy: 0.6985 - loss: 1.0492 - val_categorical_accuracy: 0.7313 - val_loss: 0.9408 - learning_rate: 0.0010
Epoch 16/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.6857 - loss: 1.0606 - val_categorical_accuracy: 0.7313 - val_loss: 0.9437 - learning_rate: 0.0010
Epoch 17/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - categorical_accuracy: 0.6850 - loss: 1.0511 - val_categorical_accuracy: 0.7313 - val_loss: 0.9183 - learning_rate: 0.0010
Epoch 18/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6957 - loss: 1.0631 - val_categorical_accuracy: 0.7313 - val_loss: 0.9362 - learning_rate: 0.0010
Epoch 19/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6985 - loss: 1.0554 - val_categorical_accuracy: 0.7313 - val_loss: 0.9446 - learning_rate: 0.0010
Epoch 20/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7000 - loss: 1.0847 - val_categorical_accuracy: 0.7313 - val_loss: 0.9579 - learning_rate: 0.0010
Epoch 21/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7008 - loss: 1.0586 - val_categorical_accuracy: 0.7313 - val_loss: 0.9362 - learning_rate: 0.0010
Epoch 22/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7034 - loss: 1.0484 - val_categorical_accuracy: 0.7313 - val_loss: 0.9358 - learning_rate: 0.0010
Epoch 23/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7063 - loss: 1.0662 - val_categorical_accuracy: 0.7313 - val_loss: 0.9393 - learning_rate: 1.0000e-07
Epoch 24/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7060 - loss: 1.0935 - val_categorical_accuracy: 0.7313 - val_loss: 0.9378 - learning_rate: 1.0000e-07
Epoch 25/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6844 - loss: 1.0563 - val_categorical_accuracy: 0.7313 - val_loss: 0.9408 - learning_rate: 1.0000e-07
Epoch 26/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7142 - loss: 1.0623 - val_categorical_accuracy: 0.7313 - val_loss: 0.9408 - learning_rate: 1.0000e-07
Epoch 27/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.6977 - loss: 0.9932 - val_categorical_accuracy: 0.7313 - val_loss: 0.9408 - learning_rate: 1.0000e-07
Epoch 28/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6958 - loss: 0.9784 - val_categorical_accuracy: 0.7313 - val_loss: 0.9371 - learning_rate: 1.0000e-11
Epoch 29/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7098 - loss: 1.1110 - val_categorical_accuracy: 0.7313 - val_loss: 0.9392 - learning_rate: 1.0000e-11
Epoch 30/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6995 - loss: 1.0303 - val_categorical_accuracy: 0.7313 - val_loss: 0.9401 - learning_rate: 1.0000e-11
Epoch 31/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7101 - loss: 1.0431 - val_categorical_accuracy: 0.7313 - val_loss: 0.9377 - learning_rate: 1.0000e-11
Epoch 32/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7075 - loss: 1.0574 - val_categorical_accuracy: 0.7313 - val_loss: 0.9391 - learning_rate: 1.0000e-11
Epoch 33/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7069 - loss: 1.0412 - val_categorical_accuracy: 0.7313 - val_loss: 0.9390 - learning_rate: 1.0000e-15
Epoch 34/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7160 - loss: 0.9550 - val_categorical_accuracy: 0.7313 - val_loss: 0.9397 - learning_rate: 1.0000e-15
Epoch 35/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7059 - loss: 1.0223 - val_categorical_accuracy: 0.7313 - val_loss: 0.9379 - learning_rate: 1.0000e-15
Epoch 36/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.7140 - loss: 0.9778 - val_categorical_accuracy: 0.7313 - val_loss: 0.9359 - learning_rate: 1.0000e-15
Epoch 37/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7102 - loss: 1.0042 - val_categorical_accuracy: 0.7313 - val_loss: 0.9378 - learning_rate: 1.0000e-15
Epoch 38/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - categorical_accuracy: 0.7085 - loss: 1.0708 - val_categorical_accuracy: 0.7313 - val_loss: 0.9369 - learning_rate: 1.0000e-19
Epoch 39/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - categorical_accuracy: 0.7073 - loss: 1.0794 - val_categorical_accuracy: 0.7313 - val_loss: 0.9367 - learning_rate: 1.0000e-19
Epoch 40/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7070 - loss: 1.0221 - val_categorical_accuracy: 0.7313 - val_loss: 0.9352 - learning_rate: 1.0000e-19
Epoch 41/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7082 - loss: 1.0569 - val_categorical_accuracy: 0.7313 - val_loss: 0.9339 - learning_rate: 1.0000e-19
Epoch 42/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7022 - loss: 1.1002 - val_categorical_accuracy: 0.7313 - val_loss: 0.9370 - learning_rate: 1.0000e-19
Epoch 43/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7120 - loss: 1.0330 - val_categorical_accuracy: 0.7313 - val_loss: 0.9397 - learning_rate: 1.0000e-23
Epoch 44/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7064 - loss: 1.0969 - val_categorical_accuracy: 0.7313 - val_loss: 0.9399 - learning_rate: 1.0000e-23
Epoch 45/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7164 - loss: 1.0465 - val_categorical_accuracy: 0.7313 - val_loss: 0.9381 - learning_rate: 1.0000e-23
Epoch 46/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.7023 - loss: 1.0271 - val_categorical_accuracy: 0.7313 - val_loss: 0.9380 - learning_rate: 1.0000e-23
Epoch 47/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - categorical_accuracy: 0.6979 - loss: 1.0535 - val_categorical_accuracy: 0.7313 - val_loss: 0.9373 - learning_rate: 1.0000e-23
Epoch 48/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7220 - loss: 1.1205 - val_categorical_accuracy: 0.7313 - val_loss: 0.9348 - learning_rate: 1.0000e-27
Epoch 49/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7045 - loss: 1.0332 - val_categorical_accuracy: 0.7313 - val_loss: 0.9367 - learning_rate: 1.0000e-27
Epoch 50/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - categorical_accuracy: 0.7011 - loss: 1.0925 - val_categorical_accuracy: 0.7313 - val_loss: 0.9350 - learning_rate: 1.0000e-27
Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 88ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :   Data Description                                 Model Description  Train Accuracy  Test Accuracy  F1 Score
0  Glove Basic Data  NN Classifier Layers-Dropouts-Normalization-ADAM           74.25          73.81     62.69


#TFIDF Basic
modelDesc = 'NN Classifier Layers-Dropouts-Normalization-ADAM'
dataDesc = 'TFIDF Basic Data'
model_fitted,history = modelNN_withDropoutsAndNormalization_Adam(X_train_tfidf,y_train_tfidf_cat)
combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=model_fitted,historyNN=history,Accuracy='categorical_accuracy',ValidationAccuracy='val_categorical_accuracy',DataDesc=dataDesc,ModelDesc=modelDesc,X_train=X_train_tfidf,Y_train=y_train_tfidf_cat,X_test=X_test_tfidf,Y_test=y_test_tfidf_cat)],ignore_index=False,sort=False)

Model: "sequential_8"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ dense_29 (Dense)                     │ (None, 150)                 │         425,850 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_15 (Dropout)                 │ (None, 150)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_15               │ (None, 150)                 │             600 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_30 (Dense)                     │ (None, 100)                 │          15,100 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_16 (Dropout)                 │ (None, 100)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_16               │ (None, 100)                 │             400 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_31 (Dense)                     │ (None, 50)                  │           5,050 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_17 (Dropout)                 │ (None, 50)                  │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ batch_normalization_17               │ (None, 50)                  │             200 │
│ (BatchNormalization)                 │                             │                 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_32 (Dense)                     │ (None, 5)                   │             255 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 447,455 (1.71 MB)

 Trainable params: 446,855 (1.70 MB)

 Non-trainable params: 600 (2.34 KB)

Epoch 1/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 6s 25ms/step - categorical_accuracy: 0.1670 - loss: 2.0218 - val_categorical_accuracy: 0.6866 - val_loss: 1.5503 - learning_rate: 0.0010
Epoch 2/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - categorical_accuracy: 0.2315 - loss: 1.7950 - val_categorical_accuracy: 0.7313 - val_loss: 1.4295 - learning_rate: 0.0010
Epoch 3/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - categorical_accuracy: 0.3106 - loss: 1.6298 - val_categorical_accuracy: 0.7313 - val_loss: 1.3265 - learning_rate: 0.0010
Epoch 4/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - categorical_accuracy: 0.3725 - loss: 1.4466 - val_categorical_accuracy: 0.7313 - val_loss: 1.2538 - learning_rate: 0.0010
Epoch 5/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - categorical_accuracy: 0.4462 - loss: 1.4082 - val_categorical_accuracy: 0.7313 - val_loss: 1.1735 - learning_rate: 0.0010
Epoch 6/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.5558 - loss: 1.3027 - val_categorical_accuracy: 0.7313 - val_loss: 1.1118 - learning_rate: 0.0010
Epoch 7/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - categorical_accuracy: 0.5912 - loss: 1.2012 - val_categorical_accuracy: 0.7313 - val_loss: 1.0662 - learning_rate: 0.0010
Epoch 8/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.6146 - loss: 1.2727 - val_categorical_accuracy: 0.7313 - val_loss: 1.0320 - learning_rate: 0.0010
Epoch 9/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.6303 - loss: 1.0882 - val_categorical_accuracy: 0.7313 - val_loss: 0.9973 - learning_rate: 0.0010
Epoch 10/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.6443 - loss: 1.1274 - val_categorical_accuracy: 0.7313 - val_loss: 0.9767 - learning_rate: 0.0010
Epoch 11/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.6483 - loss: 1.1798 - val_categorical_accuracy: 0.7313 - val_loss: 0.9747 - learning_rate: 0.0010
Epoch 12/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - categorical_accuracy: 0.6818 - loss: 1.0227 - val_categorical_accuracy: 0.7313 - val_loss: 0.9608 - learning_rate: 0.0010
Epoch 13/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - categorical_accuracy: 0.6891 - loss: 1.0578 - val_categorical_accuracy: 0.7313 - val_loss: 0.9589 - learning_rate: 0.0010
Epoch 14/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.6859 - loss: 1.0683 - val_categorical_accuracy: 0.7313 - val_loss: 0.9419 - learning_rate: 0.0010
Epoch 15/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - categorical_accuracy: 0.6875 - loss: 1.0947 - val_categorical_accuracy: 0.7313 - val_loss: 0.9626 - learning_rate: 0.0010
Epoch 16/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - categorical_accuracy: 0.6953 - loss: 0.9802 - val_categorical_accuracy: 0.7313 - val_loss: 0.9631 - learning_rate: 0.0010
Epoch 17/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - categorical_accuracy: 0.6740 - loss: 1.0106 - val_categorical_accuracy: 0.7313 - val_loss: 0.9475 - learning_rate: 0.0010
Epoch 18/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - categorical_accuracy: 0.6994 - loss: 0.9488 - val_categorical_accuracy: 0.7313 - val_loss: 0.9426 - learning_rate: 0.0010
Epoch 19/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - categorical_accuracy: 0.7229 - loss: 0.9136 - val_categorical_accuracy: 0.7313 - val_loss: 0.9430 - learning_rate: 0.0010
Epoch 20/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - categorical_accuracy: 0.7051 - loss: 0.9425 - val_categorical_accuracy: 0.7313 - val_loss: 0.9380 - learning_rate: 1.0000e-07
Epoch 21/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - categorical_accuracy: 0.7044 - loss: 0.9101 - val_categorical_accuracy: 0.7313 - val_loss: 0.9343 - learning_rate: 1.0000e-07
Epoch 22/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - categorical_accuracy: 0.7112 - loss: 0.9260 - val_categorical_accuracy: 0.7313 - val_loss: 0.9331 - learning_rate: 1.0000e-07
Epoch 23/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - categorical_accuracy: 0.7213 - loss: 0.9235 - val_categorical_accuracy: 0.7313 - val_loss: 0.9316 - learning_rate: 1.0000e-07
Epoch 24/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - categorical_accuracy: 0.7092 - loss: 0.9600 - val_categorical_accuracy: 0.7313 - val_loss: 0.9340 - learning_rate: 1.0000e-07
Epoch 25/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - categorical_accuracy: 0.7304 - loss: 0.9073 - val_categorical_accuracy: 0.7313 - val_loss: 0.9356 - learning_rate: 1.0000e-07
Epoch 26/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - categorical_accuracy: 0.7051 - loss: 1.0097 - val_categorical_accuracy: 0.7313 - val_loss: 0.9367 - learning_rate: 1.0000e-07
Epoch 27/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - categorical_accuracy: 0.7247 - loss: 0.9209 - val_categorical_accuracy: 0.7313 - val_loss: 0.9374 - learning_rate: 1.0000e-07
Epoch 28/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.7218 - loss: 0.9560 - val_categorical_accuracy: 0.7313 - val_loss: 0.9403 - learning_rate: 1.0000e-07
Epoch 29/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - categorical_accuracy: 0.7263 - loss: 0.9201 - val_categorical_accuracy: 0.7313 - val_loss: 0.9391 - learning_rate: 1.0000e-11
Epoch 30/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - categorical_accuracy: 0.7099 - loss: 0.9445 - val_categorical_accuracy: 0.7313 - val_loss: 0.9381 - learning_rate: 1.0000e-11
Epoch 31/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - categorical_accuracy: 0.7148 - loss: 0.9073 - val_categorical_accuracy: 0.7313 - val_loss: 0.9388 - learning_rate: 1.0000e-11
Epoch 32/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - categorical_accuracy: 0.6896 - loss: 0.9963 - val_categorical_accuracy: 0.7313 - val_loss: 0.9404 - learning_rate: 1.0000e-11
Epoch 33/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.7012 - loss: 1.0028 - val_categorical_accuracy: 0.7313 - val_loss: 0.9386 - learning_rate: 1.0000e-11
Epoch 34/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - categorical_accuracy: 0.7136 - loss: 0.9999 - val_categorical_accuracy: 0.7313 - val_loss: 0.9377 - learning_rate: 1.0000e-15
Epoch 35/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - categorical_accuracy: 0.7029 - loss: 0.9994 - val_categorical_accuracy: 0.7313 - val_loss: 0.9402 - learning_rate: 1.0000e-15
Epoch 36/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - categorical_accuracy: 0.7068 - loss: 1.0131 - val_categorical_accuracy: 0.7313 - val_loss: 0.9406 - learning_rate: 1.0000e-15
Epoch 37/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - categorical_accuracy: 0.7183 - loss: 0.9534 - val_categorical_accuracy: 0.7313 - val_loss: 0.9392 - learning_rate: 1.0000e-15
Epoch 38/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - categorical_accuracy: 0.7194 - loss: 1.0133 - val_categorical_accuracy: 0.7313 - val_loss: 0.9383 - learning_rate: 1.0000e-15
Epoch 39/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - categorical_accuracy: 0.6747 - loss: 0.9125 - val_categorical_accuracy: 0.7313 - val_loss: 0.9411 - learning_rate: 1.0000e-19
Epoch 40/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - categorical_accuracy: 0.7113 - loss: 0.9831 - val_categorical_accuracy: 0.7313 - val_loss: 0.9407 - learning_rate: 1.0000e-19
Epoch 41/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.7169 - loss: 0.9915 - val_categorical_accuracy: 0.7313 - val_loss: 0.9392 - learning_rate: 1.0000e-19
Epoch 42/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - categorical_accuracy: 0.7117 - loss: 0.9633 - val_categorical_accuracy: 0.7313 - val_loss: 0.9399 - learning_rate: 1.0000e-19
Epoch 43/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.7260 - loss: 0.9557 - val_categorical_accuracy: 0.7313 - val_loss: 0.9392 - learning_rate: 1.0000e-19
Epoch 44/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 32ms/step - categorical_accuracy: 0.7207 - loss: 0.8749 - val_categorical_accuracy: 0.7313 - val_loss: 0.9394 - learning_rate: 1.0000e-23
Epoch 45/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - categorical_accuracy: 0.7156 - loss: 0.9323 - val_categorical_accuracy: 0.7313 - val_loss: 0.9384 - learning_rate: 1.0000e-23
Epoch 46/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - categorical_accuracy: 0.6927 - loss: 0.9083 - val_categorical_accuracy: 0.7313 - val_loss: 0.9377 - learning_rate: 1.0000e-23
Epoch 47/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - categorical_accuracy: 0.7191 - loss: 1.0012 - val_categorical_accuracy: 0.7313 - val_loss: 0.9386 - learning_rate: 1.0000e-23
Epoch 48/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - categorical_accuracy: 0.7165 - loss: 0.9832 - val_categorical_accuracy: 0.7313 - val_loss: 0.9381 - learning_rate: 1.0000e-23
Epoch 49/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - categorical_accuracy: 0.7355 - loss: 0.9439 - val_categorical_accuracy: 0.7313 - val_loss: 0.9363 - learning_rate: 1.0000e-27
Epoch 50/50
34/34 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - categorical_accuracy: 0.6594 - loss: 0.9961 - val_categorical_accuracy: 0.7313 - val_loss: 0.9356 - learning_rate: 1.0000e-27
Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 61ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :   Data Description                                 Model Description  Train Accuracy  Test Accuracy  F1 Score
0  TFIDF Basic Data  NN Classifier Layers-Dropouts-Normalization-ADAM           74.55          73.81     62.69


pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Collating Results : 
-----------------------
   Data Description                                 Model Description  Train Accuracy  Test Accuracy  F1 Score
0         W2V Basic                          Basic Model with 1 layer           78.44          75.00     65.16
0  Glove Basic Data                          Basic Model with 1 layer           91.02          73.81     69.07
0  TFIDF Basic Data                          Basic Model with 1 layer           94.01          70.24     61.33
0    W2V Basic Data   NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  Glove Basic data   NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  TFIDF Basic data   NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0    W2V Basic Data  NN Classifier Layers-Dropouts-Normalization-ADAM           73.95          71.43     61.94
0  Glove Basic Data  NN Classifier Layers-Dropouts-Normalization-ADAM           74.25          73.81     62.69
0  TFIDF Basic Data  NN Classifier Layers-Dropouts-Normalization-ADAM           74.55          73.81     62.69


# Define the model building function for hyperparameter tuning
def build_model(hp, input_shape):
    model_ann_hyper = Sequential()

    units = hp.Int('units', min_value=10, max_value=100, step=10)
    dropout_rate1 = hp.Float('dropout_rate1', min_value=0.1, max_value=0.5, step=0.1)
    dropout_rate2 = hp.Float('dropout_rate2', min_value=0.1, max_value=0.5, step=0.1)
    l2_param = hp.Float('l2_param', min_value=1e-5, max_value=1e-2, sampling='LOG')
    learning_rate = hp.Float('learning_rate', min_value=1e-5, max_value=1e-1, sampling='LOG')

    model_ann_hyper.add(Dense(units, input_shape=(input_shape,), activation='relu', kernel_initializer='he_uniform',
                    kernel_regularizer=regularizers.l2(l2_param),
                    kernel_constraint=tf.keras.constraints.UnitNorm()))
    model_ann_hyper.add(Dropout(dropout_rate1))
    model_ann_hyper.add(BatchNormalization())
    model_ann_hyper.add(Dense(units, activation='relu', kernel_initializer='he_uniform',
                    kernel_regularizer=regularizers.l2(l2_param),
                    kernel_constraint=tf.keras.constraints.UnitNorm()))
    model_ann_hyper.add(Dropout(dropout_rate2))
    model_ann_hyper.add(BatchNormalization())
    model_ann_hyper.add(Dense(5, activation='softmax', kernel_regularizer=regularizers.l2(l2_param),
                    kernel_constraint=tf.keras.constraints.UnitNorm()))  # Multiclass classification

    # Compile the model
    opt = SGD(learning_rate=learning_rate, momentum=0.9)
    model_ann_hyper.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

    return model_ann_hyper


# Define the hyperparameter tuning function
def tune_hyperparameters(X_train, y_train, X_test, y_test):
    input_shape = X_train.shape[1]

    tuner = kt.RandomSearch(
        hypermodel=lambda hp: build_model(hp, input_shape),
        objective='val_accuracy',
        max_trials=20,  # Number of different hyperparameter combinations to try
        executions_per_trial=1,
        directory='my_dir',
        project_name='hyperparameter_tuning'
    )

    tuner.search(X_train, y_train, epochs=50, validation_data=(X_test, y_test))

    # Print the best hyperparameters
    best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
    print(f'Best hyperparameters: {best_hps.values}')

    # Retrieve the best model and training history
    #best_model = tuner.get_best_models(num_models=1)[0]
    best_model_ann = tuner.hypermodel.build(best_hps)
    best_model_ann.build(input_shape=(None, X_train.shape[1]))
    history_best_ann = best_model_ann.fit(
        X_train, y_train,
        epochs=50,
        batch_size=8,
        validation_data=(X_test, y_test),
        verbose=1
    )

    return best_model_ann, history_best_ann


def train_and_evaluate_model(X_train, y_train, X_test, y_test, descriptionOfData, seed=7):
    # Set random seeds for reproducibility
    np.random.seed(seed)
    tf.random.set_seed(seed)

    # Perform hyperparameter tuning
    best_model_ann, history_best_ann = tune_hyperparameters(X_train, y_train, X_test, y_test)

    # Evaluate the model
    _, train_accuracy = best_model_ann.evaluate(X_train, y_train, batch_size=8, verbose=0)
    _, test_accuracy = best_model_ann.evaluate(X_test, y_test, batch_size=8, verbose=0)

    print(f"\nTraining completed for dataset: {descriptionOfData}")
    print(f'Train accuracy: {train_accuracy * 100:.2f}%')
    print(f'Test accuracy: {test_accuracy * 100:.2f}%')

    return best_model_ann, history_best_ann


best_model_w2v, best_history_w2v = train_and_evaluate_model(X_train_w2v, y_train_w2v_cat, X_test_w2v, y_test_w2v_cat, "Word2Vec-Basic- with best params")

Trial 20 Complete [00h 00m 11s]
val_accuracy: 0.380952388048172

Best val_accuracy So Far: 0.738095223903656
Total elapsed time: 00h 03m 50s
Best hyperparameters: {'units': 20, 'dropout_rate1': 0.4, 'dropout_rate2': 0.4, 'l2_param': 4.2036040689040396e-05, 'learning_rate': 0.0024348956814965283}
Epoch 1/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 3s 17ms/step - accuracy: 0.2846 - loss: 1.8107 - val_accuracy: 0.4881 - val_loss: 1.3329
Epoch 2/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.5716 - loss: 1.2498 - val_accuracy: 0.7024 - val_loss: 1.0393
Epoch 3/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6469 - loss: 1.1585 - val_accuracy: 0.6071 - val_loss: 1.1894
Epoch 4/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6882 - loss: 1.0881 - val_accuracy: 0.7024 - val_loss: 1.0447
Epoch 5/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.6813 - loss: 1.1359 - val_accuracy: 0.7262 - val_loss: 0.9845
Epoch 6/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.6958 - loss: 1.0712 - val_accuracy: 0.7381 - val_loss: 0.9593
Epoch 7/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.7004 - loss: 1.0240 - val_accuracy: 0.7381 - val_loss: 0.9424
Epoch 8/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7160 - loss: 1.0553 - val_accuracy: 0.7381 - val_loss: 0.9339
Epoch 9/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7183 - loss: 1.0054 - val_accuracy: 0.7381 - val_loss: 0.9526
Epoch 10/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7166 - loss: 0.9566 - val_accuracy: 0.7381 - val_loss: 0.9517
Epoch 11/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7148 - loss: 1.0069 - val_accuracy: 0.7381 - val_loss: 0.9344
Epoch 12/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7188 - loss: 1.0166 - val_accuracy: 0.7381 - val_loss: 0.9388
Epoch 13/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7204 - loss: 1.0121 - val_accuracy: 0.7381 - val_loss: 0.9209
Epoch 14/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7193 - loss: 1.0019 - val_accuracy: 0.7381 - val_loss: 0.9251
Epoch 15/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9961 - val_accuracy: 0.7381 - val_loss: 0.9235
Epoch 16/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7238 - loss: 0.9623 - val_accuracy: 0.7381 - val_loss: 0.9218
Epoch 17/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 1.0195 - val_accuracy: 0.7381 - val_loss: 0.9212
Epoch 18/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 1.0184 - val_accuracy: 0.7381 - val_loss: 0.9119
Epoch 19/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 1.0116 - val_accuracy: 0.7381 - val_loss: 0.9069
Epoch 20/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7196 - loss: 1.0023 - val_accuracy: 0.7381 - val_loss: 0.9207
Epoch 21/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 1.0085 - val_accuracy: 0.7381 - val_loss: 0.9167
Epoch 22/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9808 - val_accuracy: 0.7381 - val_loss: 0.9159
Epoch 23/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9917 - val_accuracy: 0.7381 - val_loss: 0.9108
Epoch 24/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9895 - val_accuracy: 0.7381 - val_loss: 0.9059
Epoch 25/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9644 - val_accuracy: 0.7381 - val_loss: 0.9120
Epoch 26/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 1.0125 - val_accuracy: 0.7381 - val_loss: 0.9148
Epoch 27/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9562 - val_accuracy: 0.7381 - val_loss: 0.9151
Epoch 28/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 1.0028 - val_accuracy: 0.7381 - val_loss: 0.9144
Epoch 29/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9829 - val_accuracy: 0.7381 - val_loss: 0.9189
Epoch 30/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9809 - val_accuracy: 0.7381 - val_loss: 0.9253
Epoch 31/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9648 - val_accuracy: 0.7381 - val_loss: 0.9200
Epoch 32/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9670 - val_accuracy: 0.7381 - val_loss: 0.9181
Epoch 33/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9421 - val_accuracy: 0.7381 - val_loss: 0.9237
Epoch 34/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9524 - val_accuracy: 0.7381 - val_loss: 0.9372
Epoch 35/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9645 - val_accuracy: 0.7381 - val_loss: 0.9219
Epoch 36/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9397 - val_accuracy: 0.7381 - val_loss: 0.9256
Epoch 37/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9787 - val_accuracy: 0.7381 - val_loss: 0.9235
Epoch 38/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9822 - val_accuracy: 0.7381 - val_loss: 0.9220
Epoch 39/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9668 - val_accuracy: 0.7381 - val_loss: 0.9269
Epoch 40/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9764 - val_accuracy: 0.7381 - val_loss: 0.9255
Epoch 41/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9482 - val_accuracy: 0.7381 - val_loss: 0.9203
Epoch 42/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - accuracy: 0.7213 - loss: 0.9811 - val_accuracy: 0.7381 - val_loss: 0.9136
Epoch 43/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9832 - val_accuracy: 0.7381 - val_loss: 0.9187
Epoch 44/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9859 - val_accuracy: 0.7381 - val_loss: 0.9256
Epoch 45/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7213 - loss: 0.9644 - val_accuracy: 0.7381 - val_loss: 0.9259
Epoch 46/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7213 - loss: 0.9619 - val_accuracy: 0.7381 - val_loss: 0.9213
Epoch 47/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7213 - loss: 0.9477 - val_accuracy: 0.7381 - val_loss: 0.9243
Epoch 48/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7213 - loss: 0.9706 - val_accuracy: 0.7381 - val_loss: 0.9204
Epoch 49/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.7213 - loss: 0.9519 - val_accuracy: 0.7381 - val_loss: 0.9188
Epoch 50/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.7213 - loss: 0.9666 - val_accuracy: 0.7381 - val_loss: 0.9193

Training completed for dataset: Word2Vec-Basic- with best params
Train accuracy: 73.95%
Test accuracy: 73.81%


combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=best_model_w2v,historyNN=best_history_w2v,Accuracy='accuracy',ValidationAccuracy='val_accuracy',
                         DataDesc='W2V Basic',ModelDesc='Basic w2v NN model with best parameters',
                         X_train=X_train_w2v,Y_train=y_train_w2v_cat,X_test=X_test_w2v,Y_test=y_test_w2v_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 75ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :  Data Description                        Model Description  Train Accuracy  Test Accuracy  F1 Score
0        W2V Basic  Basic w2v NN model with best parameters           73.95          73.81     62.69


Collating Results : 
-----------------------
   Data Description                                 Model Description  Train Accuracy  Test Accuracy  F1 Score
0         W2V Basic                          Basic Model with 1 layer           78.44          75.00     65.16
0  Glove Basic Data                          Basic Model with 1 layer           91.02          73.81     69.07
0  TFIDF Basic Data                          Basic Model with 1 layer           94.01          70.24     61.33
0    W2V Basic Data   NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  Glove Basic data   NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  TFIDF Basic data   NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0    W2V Basic Data  NN Classifier Layers-Dropouts-Normalization-ADAM           73.95          71.43     61.94
0  Glove Basic Data  NN Classifier Layers-Dropouts-Normalization-ADAM           74.25          73.81     62.69
0  TFIDF Basic Data  NN Classifier Layers-Dropouts-Normalization-ADAM           74.55          73.81     62.69
0         W2V Basic           Basic w2v NN model with best parameters           73.95          73.81     62.69


best_model_tfidf, best_history_tfidf = train_and_evaluate_model(X_train_tfidf, y_train_tfidf_cat, X_test_tfidf, y_test_tfidf_cat, "Basic TFIDF model with best params")

Reloading Tuner from my_dir/hyperparameter_tuning/tuner0.json
Best hyperparameters: {'units': 20, 'dropout_rate1': 0.4, 'dropout_rate2': 0.30000000000000004, 'l2_param': 4.815571534978457e-05, 'learning_rate': 0.0033279189463431497}
Epoch 1/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 2s 11ms/step - accuracy: 0.2686 - loss: 1.8562 - val_accuracy: 0.6429 - val_loss: 1.2004
Epoch 2/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.5496 - loss: 1.3043 - val_accuracy: 0.7381 - val_loss: 0.9445
Epoch 3/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.6883 - loss: 1.0997 - val_accuracy: 0.7381 - val_loss: 0.9355
Epoch 4/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7018 - loss: 1.0819 - val_accuracy: 0.7381 - val_loss: 0.9360
Epoch 5/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7117 - loss: 1.0185 - val_accuracy: 0.7381 - val_loss: 0.9283
Epoch 6/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7178 - loss: 1.0278 - val_accuracy: 0.7381 - val_loss: 0.9309
Epoch 7/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7116 - loss: 1.0490 - val_accuracy: 0.7381 - val_loss: 0.9294
Epoch 8/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7182 - loss: 1.0329 - val_accuracy: 0.7381 - val_loss: 0.9272
Epoch 9/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7206 - loss: 1.0056 - val_accuracy: 0.7381 - val_loss: 0.9221
Epoch 10/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7200 - loss: 0.9746 - val_accuracy: 0.7381 - val_loss: 0.9187
Epoch 11/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 1.0317 - val_accuracy: 0.7381 - val_loss: 0.9167
Epoch 12/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7201 - loss: 1.0225 - val_accuracy: 0.7381 - val_loss: 0.9264
Epoch 13/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7181 - loss: 1.0121 - val_accuracy: 0.7381 - val_loss: 0.9278
Epoch 14/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9873 - val_accuracy: 0.7381 - val_loss: 0.9284
Epoch 15/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9916 - val_accuracy: 0.7381 - val_loss: 0.9192
Epoch 16/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 1.0247 - val_accuracy: 0.7381 - val_loss: 0.9193
Epoch 17/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7213 - loss: 1.0029 - val_accuracy: 0.7381 - val_loss: 0.9189
Epoch 18/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 1.0264 - val_accuracy: 0.7381 - val_loss: 0.9159
Epoch 19/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9776 - val_accuracy: 0.7381 - val_loss: 0.9153
Epoch 20/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9819 - val_accuracy: 0.7381 - val_loss: 0.9202
Epoch 21/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7213 - loss: 1.0392 - val_accuracy: 0.7381 - val_loss: 0.9166
Epoch 22/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9787 - val_accuracy: 0.7381 - val_loss: 0.9189
Epoch 23/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9977 - val_accuracy: 0.7381 - val_loss: 0.9138
Epoch 24/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9745 - val_accuracy: 0.7381 - val_loss: 0.9119
Epoch 25/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9705 - val_accuracy: 0.7381 - val_loss: 0.9122
Epoch 26/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9452 - val_accuracy: 0.7381 - val_loss: 0.9130
Epoch 27/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 1.0142 - val_accuracy: 0.7381 - val_loss: 0.9199
Epoch 28/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9985 - val_accuracy: 0.7381 - val_loss: 0.9147
Epoch 29/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7213 - loss: 0.9906 - val_accuracy: 0.7381 - val_loss: 0.9205
Epoch 30/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7213 - loss: 0.9724 - val_accuracy: 0.7381 - val_loss: 0.9230
Epoch 31/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.7213 - loss: 0.9641 - val_accuracy: 0.7381 - val_loss: 0.9062
Epoch 32/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.7213 - loss: 0.9960 - val_accuracy: 0.7381 - val_loss: 0.9047
Epoch 33/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.7213 - loss: 0.9715 - val_accuracy: 0.7381 - val_loss: 0.9015
Epoch 34/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.7213 - loss: 0.9812 - val_accuracy: 0.7381 - val_loss: 0.9073
Epoch 35/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.7213 - loss: 0.9886 - val_accuracy: 0.7381 - val_loss: 0.9065
Epoch 36/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9701 - val_accuracy: 0.7381 - val_loss: 0.8993
Epoch 37/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9753 - val_accuracy: 0.7381 - val_loss: 0.9019
Epoch 38/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9893 - val_accuracy: 0.7381 - val_loss: 0.9106
Epoch 39/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9771 - val_accuracy: 0.7381 - val_loss: 0.9145
Epoch 40/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9646 - val_accuracy: 0.7381 - val_loss: 0.9176
Epoch 41/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9734 - val_accuracy: 0.7381 - val_loss: 0.9183
Epoch 42/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9798 - val_accuracy: 0.7381 - val_loss: 0.9154
Epoch 43/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9578 - val_accuracy: 0.7381 - val_loss: 0.9172
Epoch 44/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9828 - val_accuracy: 0.7381 - val_loss: 0.9139
Epoch 45/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9914 - val_accuracy: 0.7381 - val_loss: 0.9113
Epoch 46/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9578 - val_accuracy: 0.7381 - val_loss: 0.9227
Epoch 47/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9929 - val_accuracy: 0.7381 - val_loss: 0.9152
Epoch 48/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - accuracy: 0.7213 - loss: 0.9442 - val_accuracy: 0.7381 - val_loss: 0.9196
Epoch 49/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9391 - val_accuracy: 0.7381 - val_loss: 0.9209
Epoch 50/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7213 - loss: 0.9686 - val_accuracy: 0.7381 - val_loss: 0.9259

Training completed for dataset: Basic TFIDF model with best params
Train accuracy: 73.95%
Test accuracy: 73.81%


combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=best_model_tfidf, historyNN=best_history_tfidf,Accuracy='accuracy',ValidationAccuracy='val_accuracy',
                         DataDesc='TFIDF Basic',ModelDesc='NN Classifier Model Basic TFIDF with best parameters',
                         X_train=X_train_tfidf,Y_train=y_train_tfidf_cat,X_test=X_test_tfidf,Y_test=y_test_tfidf_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 41ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :  Data Description                                  Model Description  Train Accuracy  Test Accuracy  F1 Score
0      TFIDF Basic  NN Classifier Model Basic TFIDF with best para...           73.95          73.81     62.69


Collating Results : 
-----------------------
   Data Description                                  Model Description  Train Accuracy  Test Accuracy  F1 Score
0         W2V Basic                           Basic Model with 1 layer           78.44          75.00     65.16
0  Glove Basic Data                           Basic Model with 1 layer           91.02          73.81     69.07
0  TFIDF Basic Data                           Basic Model with 1 layer           94.01          70.24     61.33
0    W2V Basic Data    NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  Glove Basic data    NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  TFIDF Basic data    NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0    W2V Basic Data   NN Classifier Layers-Dropouts-Normalization-ADAM           73.95          71.43     61.94
0  Glove Basic Data   NN Classifier Layers-Dropouts-Normalization-ADAM           74.25          73.81     62.69
0  TFIDF Basic Data   NN Classifier Layers-Dropouts-Normalization-ADAM           74.55          73.81     62.69
0         W2V Basic            Basic w2v NN model with best parameters           73.95          73.81     62.69
0       TFIDF Basic  NN Classifier Model Basic TFIDF with best para...           73.95          73.81     62.69


def build_model_rnn(input_shape):
    model_rnn = Sequential()

    # hyperparameters
    rnn_units = 151
    dropout_rate1 = 0.3
    dropout_rate2 = 0.4
    l2_param = 1e-4
    learning_rate = 0.01

    model_rnn.add(SimpleRNN(rnn_units, input_shape=input_shape, return_sequences=True,
                        kernel_initializer='he_uniform',
                        kernel_regularizer=regularizers.l2(l2_param),
                        kernel_constraint=tf.keras.constraints.UnitNorm()))
    model_rnn.add(Dropout(dropout_rate1))
    model_rnn.add(BatchNormalization())

    model_rnn.add(SimpleRNN(rnn_units, return_sequences=False,
                        kernel_initializer='he_uniform',
                        kernel_regularizer=regularizers.l2(l2_param),
                        kernel_constraint=tf.keras.constraints.UnitNorm()))
    model_rnn.add(Dropout(dropout_rate2))
    model_rnn.add(BatchNormalization())

    model_rnn.add(Dense(5, activation='softmax', kernel_regularizer=regularizers.l2(l2_param),
                    kernel_constraint=tf.keras.constraints.UnitNorm()))  # Multiclass classification

    # Compile the model
    opt = SGD(learning_rate=learning_rate, momentum=0.9)
    model_rnn.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

    return model_rnn


def train_and_evaluate_model_rnn(X_train, y_train, X_test, y_test, descriptionOfData, seed=7):
    # Set random seeds for reproducibility
    np.random.seed(seed)
    tf.random.set_seed(seed)

    if len(X_train.shape) == 2:
        X_train = np.expand_dims(X_train, axis=1)  # Adding time steps dimension
        X_test = np.expand_dims(X_test, axis=1)    # Adding time steps dimension

    # Build the model with fixed parameters
    input_shape = (X_train.shape[1], X_train.shape[2])  # (timesteps, features)
    model_rnn = build_model_rnn(input_shape)

    # Train the model
    history_rnn = model_rnn.fit(
        X_train, y_train,
        epochs=50,
        batch_size=8,
        validation_data=(X_test, y_test),
        verbose=1
    )

    return model_rnn, history_rnn


X_train_w2v.shape[0]

334


best_model_w2v_rnn, best_history_w2v_rnn = train_and_evaluate_model_rnn(X_train_w2v, y_train_w2v_cat, X_test_w2v, y_test_w2v_cat, "RNN Model on Word2Vec")

Epoch 1/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 4s 26ms/step - accuracy: 0.3427 - loss: 1.8175 - val_accuracy: 0.7381 - val_loss: 1.1011
Epoch 2/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.6340 - loss: 1.1896 - val_accuracy: 0.7381 - val_loss: 1.2502
Epoch 3/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6311 - loss: 1.3178 - val_accuracy: 0.7381 - val_loss: 1.1766
Epoch 4/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.6392 - loss: 1.2975 - val_accuracy: 0.7381 - val_loss: 1.1855
Epoch 5/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6482 - loss: 1.2564 - val_accuracy: 0.7381 - val_loss: 1.1565
Epoch 6/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.6924 - loss: 1.1248 - val_accuracy: 0.7381 - val_loss: 1.1277
Epoch 7/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6661 - loss: 1.1888 - val_accuracy: 0.7381 - val_loss: 1.1460
Epoch 8/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6757 - loss: 1.2504 - val_accuracy: 0.7381 - val_loss: 1.1988
Epoch 9/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6840 - loss: 1.1586 - val_accuracy: 0.7381 - val_loss: 1.2066
Epoch 10/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6574 - loss: 1.2662 - val_accuracy: 0.6905 - val_loss: 1.1332
Epoch 11/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6871 - loss: 1.1691 - val_accuracy: 0.7381 - val_loss: 1.1612
Epoch 12/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6573 - loss: 1.1964 - val_accuracy: 0.7143 - val_loss: 1.1582
Epoch 13/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7029 - loss: 1.1824 - val_accuracy: 0.7381 - val_loss: 1.1996
Epoch 14/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.6837 - loss: 1.1593 - val_accuracy: 0.7381 - val_loss: 1.1417
Epoch 15/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6839 - loss: 1.2439 - val_accuracy: 0.6667 - val_loss: 1.1480
Epoch 16/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6899 - loss: 1.1670 - val_accuracy: 0.7381 - val_loss: 1.1027
Epoch 17/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6885 - loss: 1.1549 - val_accuracy: 0.7024 - val_loss: 1.0993
Epoch 18/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6757 - loss: 1.2118 - val_accuracy: 0.7381 - val_loss: 1.1284
Epoch 19/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.6800 - loss: 1.1636 - val_accuracy: 0.7381 - val_loss: 1.0652
Epoch 20/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.7068 - loss: 1.0971 - val_accuracy: 0.7381 - val_loss: 1.0709
Epoch 21/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7025 - loss: 1.1562 - val_accuracy: 0.7024 - val_loss: 1.1726
Epoch 22/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.6983 - loss: 1.1250 - val_accuracy: 0.7381 - val_loss: 1.1407
Epoch 23/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.6986 - loss: 1.0856 - val_accuracy: 0.7381 - val_loss: 1.0853
Epoch 24/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.6985 - loss: 1.1309 - val_accuracy: 0.7381 - val_loss: 1.0773
Epoch 25/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7209 - loss: 1.0321 - val_accuracy: 0.7381 - val_loss: 1.1094
Epoch 26/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6863 - loss: 1.1364 - val_accuracy: 0.7381 - val_loss: 1.0777
Epoch 27/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6986 - loss: 1.1156 - val_accuracy: 0.7381 - val_loss: 1.0315
Epoch 28/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7073 - loss: 1.0926 - val_accuracy: 0.7143 - val_loss: 1.1013
Epoch 29/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.7087 - loss: 1.0741 - val_accuracy: 0.6667 - val_loss: 1.0780
Epoch 30/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - accuracy: 0.7081 - loss: 1.0664 - val_accuracy: 0.7381 - val_loss: 1.0560
Epoch 31/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7000 - loss: 1.0788 - val_accuracy: 0.7381 - val_loss: 1.0124
Epoch 32/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7016 - loss: 1.0802 - val_accuracy: 0.7381 - val_loss: 1.0070
Epoch 33/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7111 - loss: 1.1164 - val_accuracy: 0.7381 - val_loss: 1.0168
Epoch 34/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.7135 - loss: 1.0554 - val_accuracy: 0.7381 - val_loss: 1.0605
Epoch 35/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7117 - loss: 1.0570 - val_accuracy: 0.7381 - val_loss: 1.0501
Epoch 36/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7068 - loss: 1.1104 - val_accuracy: 0.7381 - val_loss: 1.0625
Epoch 37/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.6961 - loss: 1.0433 - val_accuracy: 0.7381 - val_loss: 1.0688
Epoch 38/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7087 - loss: 1.0558 - val_accuracy: 0.7262 - val_loss: 1.0700
Epoch 39/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.7149 - loss: 1.0366 - val_accuracy: 0.6548 - val_loss: 1.0558
Epoch 40/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.7039 - loss: 1.0264 - val_accuracy: 0.7381 - val_loss: 0.9760
Epoch 41/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7135 - loss: 0.9796 - val_accuracy: 0.6667 - val_loss: 1.0620
Epoch 42/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.7063 - loss: 1.0474 - val_accuracy: 0.7381 - val_loss: 0.9906
Epoch 43/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.7132 - loss: 1.0315 - val_accuracy: 0.7381 - val_loss: 1.0376
Epoch 44/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.7010 - loss: 1.0814 - val_accuracy: 0.7381 - val_loss: 0.9901
Epoch 45/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.7074 - loss: 1.0355 - val_accuracy: 0.7381 - val_loss: 1.0059
Epoch 46/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.7222 - loss: 1.0447 - val_accuracy: 0.7381 - val_loss: 1.0025
Epoch 47/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7175 - loss: 1.0290 - val_accuracy: 0.7381 - val_loss: 1.0021
Epoch 48/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.7213 - loss: 1.0595 - val_accuracy: 0.7381 - val_loss: 1.0592
Epoch 49/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7176 - loss: 1.0336 - val_accuracy: 0.7381 - val_loss: 0.9653
Epoch 50/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.7164 - loss: 0.9866 - val_accuracy: 0.7381 - val_loss: 0.9194


combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=best_model_w2v_rnn, historyNN=best_history_w2v_rnn,Accuracy='accuracy',ValidationAccuracy='val_accuracy',
                         DataDesc='W2V Basic',ModelDesc='RNN Classifier Basic W2V model',
                         X_train=np.expand_dims(X_train_w2v, axis=1),Y_train=y_train_w2v_cat,X_test=np.expand_dims(X_test_w2v, axis=1),Y_test=y_test_w2v_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1000)
#print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 1s 154ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :  Data Description               Model Description  Train Accuracy  Test Accuracy  F1 Score
0        W2V Basic  RNN Classifier Basic W2V model           73.95          73.81     62.69


best_model_tfidf_rnn, best_history_tfidf_rnn = train_and_evaluate_model_rnn(X_train_tfidf, y_train_tfidf_cat, X_test_tfidf, y_test_tfidf_cat, "RNN Classifier with Basic TFIDF ")

Epoch 1/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 4s 26ms/step - accuracy: 0.2709 - loss: 1.8196 - val_accuracy: 0.7381 - val_loss: 1.1996
Epoch 2/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6089 - loss: 1.2875 - val_accuracy: 0.7381 - val_loss: 1.1543
Epoch 3/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6484 - loss: 1.2782 - val_accuracy: 0.7381 - val_loss: 1.1461
Epoch 4/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6560 - loss: 1.2436 - val_accuracy: 0.7381 - val_loss: 1.1155
Epoch 5/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - accuracy: 0.6727 - loss: 1.1882 - val_accuracy: 0.7381 - val_loss: 1.1297
Epoch 6/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - accuracy: 0.6974 - loss: 1.1857 - val_accuracy: 0.7262 - val_loss: 1.2564
Epoch 7/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - accuracy: 0.6806 - loss: 1.2141 - val_accuracy: 0.7381 - val_loss: 1.1449
Epoch 8/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - accuracy: 0.6604 - loss: 1.2076 - val_accuracy: 0.7024 - val_loss: 1.2469
Epoch 9/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6856 - loss: 1.2978 - val_accuracy: 0.6190 - val_loss: 1.2333
Epoch 10/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6730 - loss: 1.2896 - val_accuracy: 0.6786 - val_loss: 1.2349
Epoch 11/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6825 - loss: 1.2674 - val_accuracy: 0.7381 - val_loss: 1.1568
Epoch 12/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6947 - loss: 1.2743 - val_accuracy: 0.7262 - val_loss: 1.1742
Epoch 13/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6886 - loss: 1.2174 - val_accuracy: 0.7381 - val_loss: 1.1936
Epoch 14/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6727 - loss: 1.2648 - val_accuracy: 0.6667 - val_loss: 1.1902
Epoch 15/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6578 - loss: 1.2619 - val_accuracy: 0.6190 - val_loss: 1.2607
Epoch 16/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6742 - loss: 1.2050 - val_accuracy: 0.7381 - val_loss: 1.1905
Epoch 17/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6896 - loss: 1.1921 - val_accuracy: 0.6190 - val_loss: 1.2109
Epoch 18/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6736 - loss: 1.1994 - val_accuracy: 0.7381 - val_loss: 1.1190
Epoch 19/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.6908 - loss: 1.1071 - val_accuracy: 0.5238 - val_loss: 1.2815
Epoch 20/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6859 - loss: 1.1436 - val_accuracy: 0.7381 - val_loss: 1.1164
Epoch 21/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6729 - loss: 1.1660 - val_accuracy: 0.6667 - val_loss: 1.1523
Epoch 22/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6544 - loss: 1.2532 - val_accuracy: 0.7381 - val_loss: 1.0878
Epoch 23/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6695 - loss: 1.0854 - val_accuracy: 0.7381 - val_loss: 1.0821
Epoch 24/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7201 - loss: 1.0802 - val_accuracy: 0.7381 - val_loss: 1.1245
Epoch 25/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.6872 - loss: 1.1414 - val_accuracy: 0.7381 - val_loss: 1.1266
Epoch 26/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.6946 - loss: 1.0917 - val_accuracy: 0.7143 - val_loss: 1.1901
Epoch 27/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - accuracy: 0.6857 - loss: 1.1205 - val_accuracy: 0.6667 - val_loss: 1.1308
Epoch 28/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - accuracy: 0.6480 - loss: 1.1747 - val_accuracy: 0.7381 - val_loss: 1.0773
Epoch 29/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - accuracy: 0.6992 - loss: 1.0567 - val_accuracy: 0.7381 - val_loss: 1.1067
Epoch 30/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - accuracy: 0.7001 - loss: 1.0761 - val_accuracy: 0.7381 - val_loss: 1.0734
Epoch 31/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.6986 - loss: 1.1087 - val_accuracy: 0.7381 - val_loss: 1.0215
Epoch 32/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7185 - loss: 1.0423 - val_accuracy: 0.7381 - val_loss: 1.0478
Epoch 33/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7129 - loss: 1.0552 - val_accuracy: 0.6667 - val_loss: 1.0737
Epoch 34/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.6975 - loss: 1.0316 - val_accuracy: 0.7381 - val_loss: 1.0738
Epoch 35/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7100 - loss: 1.0882 - val_accuracy: 0.6667 - val_loss: 1.0983
Epoch 36/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7083 - loss: 1.0993 - val_accuracy: 0.6905 - val_loss: 1.0767
Epoch 37/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7042 - loss: 1.0597 - val_accuracy: 0.7381 - val_loss: 0.9892
Epoch 38/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7076 - loss: 1.0256 - val_accuracy: 0.7381 - val_loss: 1.0298
Epoch 39/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.7191 - loss: 1.0054 - val_accuracy: 0.7381 - val_loss: 1.0238
Epoch 40/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7158 - loss: 1.0261 - val_accuracy: 0.7381 - val_loss: 1.0465
Epoch 41/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7140 - loss: 1.0250 - val_accuracy: 0.7381 - val_loss: 1.0226
Epoch 42/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7305 - loss: 0.9923 - val_accuracy: 0.7024 - val_loss: 1.0371
Epoch 43/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7080 - loss: 1.0237 - val_accuracy: 0.7381 - val_loss: 0.9955
Epoch 44/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7116 - loss: 0.9966 - val_accuracy: 0.7381 - val_loss: 1.0495
Epoch 45/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.7149 - loss: 0.9648 - val_accuracy: 0.7381 - val_loss: 0.9937
Epoch 46/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7008 - loss: 1.0267 - val_accuracy: 0.7381 - val_loss: 1.0208
Epoch 47/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.7206 - loss: 0.9762 - val_accuracy: 0.7262 - val_loss: 1.0718
Epoch 48/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - accuracy: 0.7206 - loss: 0.9676 - val_accuracy: 0.7381 - val_loss: 1.0179
Epoch 49/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - accuracy: 0.7104 - loss: 1.0109 - val_accuracy: 0.7381 - val_loss: 1.0323
Epoch 50/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - accuracy: 0.7139 - loss: 0.9977 - val_accuracy: 0.7381 - val_loss: 1.0395


combinedNNModelResult = pd.concat([combinedNNModelResult, evaluationScoresandPlots(modelNN=best_model_tfidf_rnn, historyNN=best_history_tfidf_rnn,Accuracy='accuracy',ValidationAccuracy='val_accuracy',
                         DataDesc='TFIDF Basic',ModelDesc='RNN Classifier Basic TFIDF model',
                         X_train=np.expand_dims(X_train_tfidf, axis=1),Y_train=y_train_tfidf_cat,X_test=np.expand_dims(X_test_tfidf, axis=1),Y_test=y_test_tfidf_cat)],ignore_index=False,sort=False)
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 1s 147ms/step
Confusion Matrix

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :  Data Description                 Model Description  Train Accuracy  Test Accuracy  F1 Score
0      TFIDF Basic  RNN Classifier Basic TFIDF model           73.95          73.81     62.69


Collating Results : 
-----------------------
   Data Description                                  Model Description  Train Accuracy  Test Accuracy  F1 Score
0         W2V Basic                           Basic Model with 1 layer           78.44          75.00     65.16
0  Glove Basic Data                           Basic Model with 1 layer           91.02          73.81     69.07
0  TFIDF Basic Data                           Basic Model with 1 layer           94.01          70.24     61.33
0    W2V Basic Data    NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  Glove Basic data    NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0  TFIDF Basic data    NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
0    W2V Basic Data   NN Classifier Layers-Dropouts-Normalization-ADAM           73.95          71.43     61.94
0  Glove Basic Data   NN Classifier Layers-Dropouts-Normalization-ADAM           74.25          73.81     62.69
0  TFIDF Basic Data   NN Classifier Layers-Dropouts-Normalization-ADAM           74.55          73.81     62.69
0         W2V Basic            Basic w2v NN model with best parameters           73.95          73.81     62.69
0       TFIDF Basic  NN Classifier Model Basic TFIDF with best para...           73.95          73.81     62.69
0         W2V Basic                     RNN Classifier Basic W2V model           73.95          73.81     62.69
0       TFIDF Basic                   RNN Classifier Basic TFIDF model           73.95          73.81     62.69


def evaluationScoresandPlots_params(modelNN,historyNN,Accuracy,ValidationAccuracy,DataDesc,ModelDesc,X_train,Y_train,X_test,Y_test):
    print('Train and Validation Loss')
    # Capturing learning history per epoch
    hist  = pd.DataFrame(historyNN.history)
    hist['epoch'] = historyNN.epoch
    # Plotting accuracy at different epochs
    plt.plot(hist['loss'])
    plt.plot(hist['val_loss'])
    plt.legend(("Train-loss" , "Valid-loss") , loc =0)
    plt.title(f'Training and Validation Loss for data :{DataDesc} in {ModelDesc}')
    plt.show()

    print('Train and Validation Accuracy')
    # Capturing learning history per epoch
    hist  = pd.DataFrame(historyNN.history)
    hist['epoch'] = historyNN.epoch

    # Plotting accuracy at different epochs
    plt.plot(hist[Accuracy])
    plt.plot(hist[ValidationAccuracy])
    plt.legend(("Train-accuracy" , "Valid-accuracy") , loc =0)
    plt.title(f'Training and Validation Accuracy for data :{DataDesc} in {ModelDesc}')
    plt.show()

    y_test_arg=Y_test
    Y_pred_probs = modelNN.predict(X_test)  # Get predicted probabilities
    Y_pred = np.argmax(Y_pred_probs, axis=1)  # Convert probabilities to class labels

    print('Confusion Matrix')

    cm=confusion_matrix(y_test_arg,Y_pred, labels=list(range(0,5)))
    cm_df = pd.DataFrame(cm,
                        index = list(range(0,5)),
                        columns = list(range(0,5)))

    plt.figure(figsize=(10,7))
    sns.heatmap(cm_df,annot=True,fmt='d')
    plt.xlabel('Predicted')
    plt.ylabel('Truth')
    plt.show()

    #y_pred=modelNN.predict(X_test)
    #y_pred = (y_pred > 0.5)

    y_test_arg=Y_test
    Y_pred_probs = modelNN.predict(X_test)  # Get predicted probabilities
    Y_pred = np.argmax(Y_pred_probs, axis=1)
    #Accuracy as per the classification report
    cr=metrics.classification_report(y_test_arg,Y_pred)
    print(cr)

    # evaluate the keras model
    _, train_accuracy = modelNN.evaluate(X_train, Y_train, batch_size=8, verbose=0)
    _, test_accuracy = modelNN.evaluate(X_test, Y_test, batch_size=8, verbose=0)

    train_accuracy_score = round(train_accuracy*100,2)
    test_accuracy_score = round(test_accuracy*100,2)

    F1_Score=round(f1_score(y_test_arg, Y_pred, average='weighted')*100,2)


    tempResult = pd.DataFrame({'Data Description':DataDesc,
                                'Model Description':ModelDesc,
                                'Train Accuracy':train_accuracy_score,
                                'Test Accuracy':test_accuracy_score,
                                'F1 Score':F1_Score,
                                }, index=[0])
    pd.set_option('display.width',1000)
    print(f'Result Summary \n --------------------- \n :{tempResult}\n\n')
    return tempResult


# Define the model building function for hyperparameter tuning
def build_model(hp, input_shape):
    model = Sequential()

    rnn_units = hp.Int('rnn_units', min_value=50, max_value=150, step=25)
    dropout_rate1 = hp.Float('dropout_rate1', min_value=0.2, max_value=0.5, step=0.1)
    dropout_rate2 = hp.Float('dropout_rate2', min_value=0.3, max_value=0.5, step=0.1)
    l2_param = hp.Float('l2_param', min_value=1e-5, max_value=1e-2, sampling='LOG')
    learning_rate = hp.Float('learning_rate', min_value=1e-5, max_value=1e-2, sampling='LOG')

    model.add(SimpleRNN(rnn_units, input_shape=input_shape, return_sequences=True,
                        kernel_initializer='he_uniform',
                        kernel_regularizer=regularizers.l2(l2_param),
                        kernel_constraint=tf.keras.constraints.UnitNorm()))
    model.add(Dropout(dropout_rate1))
    model.add(BatchNormalization())

    model.add(SimpleRNN(rnn_units, return_sequences=False,
                        kernel_initializer='he_uniform',
                        kernel_regularizer=regularizers.l2(l2_param),
                        kernel_constraint=tf.keras.constraints.UnitNorm()))
    model.add(Dropout(dropout_rate2))
    model.add(BatchNormalization())

    model.add(Dense(5, activation='softmax', kernel_regularizer=regularizers.l2(l2_param),
                    kernel_constraint=tf.keras.constraints.UnitNorm()))  # Multiclass classification

    # Compile the model
    opt = SGD(learning_rate=learning_rate, momentum=0.9)
    model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=['sparse_categorical_accuracy'])

    return model

# Define the hyperparameter tuning function
def tune_hyperparameters(X_train, y_train, X_test, y_test):
    #input_shape = (X_train.shape[1], X_train.shape[2]) if len(X_train.shape) == 3 else (1, X_train.shape[1])
    input_shape = (X_train.shape[1], X_train.shape[2])
    tuner = kt.RandomSearch(
        hypermodel=lambda hp: build_model(hp, input_shape),
        objective='val_sparse_categorical_accuracy',
        max_trials=20,  # Number of different hyperparameter combinations to try
        executions_per_trial=1,
        directory='my_dir',
        project_name='hyperparameter_tuning'
    )

    tuner.search(X_train, y_train, epochs=50, validation_data=(X_test, y_test))

    # Print the best hyperparameters
    best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
    print(f'Best hyperparameters: {best_hps.values}')

    # Retrieve the best model and training history
    best_model = build_model(best_hps, input_shape)
    #best_model = tuner.get_best_models(num_models=1)[0]
    history = best_model.fit(
        X_train, y_train,
        epochs=50,
        batch_size=8,
        validation_data=(X_test, y_test),
        verbose=1
    )

    return best_model, history

def train_and_evaluate_model(X_train, y_train, X_test, y_test, descriptionOfData, seed=7):
    # Set random seeds for reproducibility
    np.random.seed(seed)
    tf.random.set_seed(seed)

    # Check if input data is 2D and reshape to 3D if necessary
    if len(X_train.shape) == 2:
        X_train = np.expand_dims(X_train, axis=1)  # Adding time steps dimension
        X_test = np.expand_dims(X_test, axis=1)    # Adding time steps dimension

    # Perform hyperparameter tuning
    best_model, history = tune_hyperparameters(X_train, y_train, X_test, y_test)

    # Evaluate the model
    _, train_accuracy = best_model.evaluate(X_train, y_train, batch_size=8, verbose=0)
    _, test_accuracy = best_model.evaluate(X_test, y_test, batch_size=8, verbose=0)

    print(f"\nTraining completed for dataset: {descriptionOfData}")
    print(f'Train accuracy: {train_accuracy * 100:.2f}%')
    print(f'Test accuracy: {test_accuracy * 100:.2f}%')

    # Use the evaluationScoresandPlots function for additional evaluation and plotting
    temp_result= evaluationScoresandPlots_params(best_model, history, 'sparse_categorical_accuracy', 'val_sparse_categorical_accuracy',
                             descriptionOfData, 'SimpleRNN', X_train, y_train, X_test, y_test)

    return best_model, history, temp_result


pd.set_option('display.width',1500)
best_model_w2v, best_history_w2v, temp_result = train_and_evaluate_model(X_train_w2v, y_train_w2v, X_test_w2v, y_test_w2v, "RNN-W2V Basic-with best params")
combinedNNModelResult = pd.concat([combinedNNModelResult, temp_result])
#pd.set_option('display.width',1500)
#print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Reloading Tuner from my_dir/hyperparameter_tuning/tuner0.json
Best hyperparameters: {'units': 90, 'dropout_rate1': 0.30000000000000004, 'dropout_rate2': 0.30000000000000004, 'l2_param': 0.0003604061318050688, 'learning_rate': 0.0020246419340776305}
Epoch 1/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 5s 27ms/step - loss: 1.8417 - sparse_categorical_accuracy: 0.2496 - val_loss: 1.2826 - val_sparse_categorical_accuracy: 0.6786
Epoch 2/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.4074 - sparse_categorical_accuracy: 0.4559 - val_loss: 1.1210 - val_sparse_categorical_accuracy: 0.7381
Epoch 3/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.1649 - sparse_categorical_accuracy: 0.6520 - val_loss: 0.9999 - val_sparse_categorical_accuracy: 0.7381
Epoch 4/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.1138 - sparse_categorical_accuracy: 0.6394 - val_loss: 0.9574 - val_sparse_categorical_accuracy: 0.7381
Epoch 5/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0190 - sparse_categorical_accuracy: 0.7071 - val_loss: 0.9735 - val_sparse_categorical_accuracy: 0.7381
Epoch 6/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0538 - sparse_categorical_accuracy: 0.7178 - val_loss: 0.9957 - val_sparse_categorical_accuracy: 0.7381
Epoch 7/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.1060 - sparse_categorical_accuracy: 0.6917 - val_loss: 0.9955 - val_sparse_categorical_accuracy: 0.7381
Epoch 8/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 1.0887 - sparse_categorical_accuracy: 0.6976 - val_loss: 0.9863 - val_sparse_categorical_accuracy: 0.7381
Epoch 9/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 1.0710 - sparse_categorical_accuracy: 0.7121 - val_loss: 0.9782 - val_sparse_categorical_accuracy: 0.7381
Epoch 10/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0162 - sparse_categorical_accuracy: 0.7141 - val_loss: 1.0106 - val_sparse_categorical_accuracy: 0.7381
Epoch 11/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.9926 - sparse_categorical_accuracy: 0.7085 - val_loss: 1.0189 - val_sparse_categorical_accuracy: 0.7143
Epoch 12/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 1.0154 - sparse_categorical_accuracy: 0.7049 - val_loss: 0.9961 - val_sparse_categorical_accuracy: 0.7381
Epoch 13/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0377 - sparse_categorical_accuracy: 0.7183 - val_loss: 0.9853 - val_sparse_categorical_accuracy: 0.7381
Epoch 14/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0243 - sparse_categorical_accuracy: 0.7283 - val_loss: 1.0084 - val_sparse_categorical_accuracy: 0.7381
Epoch 15/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 1.0283 - sparse_categorical_accuracy: 0.7050 - val_loss: 0.9826 - val_sparse_categorical_accuracy: 0.7381
Epoch 16/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0378 - sparse_categorical_accuracy: 0.7198 - val_loss: 0.9691 - val_sparse_categorical_accuracy: 0.7381
Epoch 17/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.9943 - sparse_categorical_accuracy: 0.7170 - val_loss: 0.9711 - val_sparse_categorical_accuracy: 0.7381
Epoch 18/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 1.0817 - sparse_categorical_accuracy: 0.7085 - val_loss: 0.9520 - val_sparse_categorical_accuracy: 0.7381
Epoch 19/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0425 - sparse_categorical_accuracy: 0.7182 - val_loss: 0.9730 - val_sparse_categorical_accuracy: 0.7381
Epoch 20/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0005 - sparse_categorical_accuracy: 0.7274 - val_loss: 0.9757 - val_sparse_categorical_accuracy: 0.7381
Epoch 21/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0349 - sparse_categorical_accuracy: 0.7100 - val_loss: 0.9869 - val_sparse_categorical_accuracy: 0.7381
Epoch 22/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 0.9971 - sparse_categorical_accuracy: 0.7139 - val_loss: 0.9907 - val_sparse_categorical_accuracy: 0.7381
Epoch 23/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0010 - sparse_categorical_accuracy: 0.7147 - val_loss: 0.9772 - val_sparse_categorical_accuracy: 0.7381
Epoch 24/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0339 - sparse_categorical_accuracy: 0.7193 - val_loss: 0.9927 - val_sparse_categorical_accuracy: 0.7381
Epoch 25/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.9976 - sparse_categorical_accuracy: 0.7230 - val_loss: 0.9658 - val_sparse_categorical_accuracy: 0.7381
Epoch 26/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 0.9858 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9597 - val_sparse_categorical_accuracy: 0.7381
Epoch 27/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 0.9648 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9573 - val_sparse_categorical_accuracy: 0.7381
Epoch 28/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.9726 - sparse_categorical_accuracy: 0.7201 - val_loss: 0.9760 - val_sparse_categorical_accuracy: 0.7381
Epoch 29/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 1.0243 - sparse_categorical_accuracy: 0.7134 - val_loss: 0.9691 - val_sparse_categorical_accuracy: 0.7381
Epoch 30/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 0.9632 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9743 - val_sparse_categorical_accuracy: 0.7381
Epoch 31/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.9597 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9768 - val_sparse_categorical_accuracy: 0.7381
Epoch 32/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.9801 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9821 - val_sparse_categorical_accuracy: 0.7381
Epoch 33/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 0.9418 - sparse_categorical_accuracy: 0.7150 - val_loss: 0.9697 - val_sparse_categorical_accuracy: 0.7381
Epoch 34/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.9279 - sparse_categorical_accuracy: 0.7216 - val_loss: 0.9904 - val_sparse_categorical_accuracy: 0.7381
Epoch 35/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.9909 - sparse_categorical_accuracy: 0.7193 - val_loss: 0.9830 - val_sparse_categorical_accuracy: 0.7381
Epoch 36/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - loss: 0.9350 - sparse_categorical_accuracy: 0.7200 - val_loss: 0.9902 - val_sparse_categorical_accuracy: 0.7381
Epoch 37/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.9923 - sparse_categorical_accuracy: 0.7200 - val_loss: 0.9706 - val_sparse_categorical_accuracy: 0.7381
Epoch 38/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - loss: 0.9836 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9843 - val_sparse_categorical_accuracy: 0.7381
Epoch 39/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9774 - sparse_categorical_accuracy: 0.7230 - val_loss: 0.9523 - val_sparse_categorical_accuracy: 0.7381
Epoch 40/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.9640 - sparse_categorical_accuracy: 0.7247 - val_loss: 0.9806 - val_sparse_categorical_accuracy: 0.7381
Epoch 41/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.9747 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9488 - val_sparse_categorical_accuracy: 0.7381
Epoch 42/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9724 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9601 - val_sparse_categorical_accuracy: 0.7381
Epoch 43/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9490 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9705 - val_sparse_categorical_accuracy: 0.7381
Epoch 44/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9592 - sparse_categorical_accuracy: 0.7241 - val_loss: 1.0064 - val_sparse_categorical_accuracy: 0.7381
Epoch 45/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 0.9437 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9688 - val_sparse_categorical_accuracy: 0.7381
Epoch 46/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 0.9532 - sparse_categorical_accuracy: 0.7105 - val_loss: 0.9675 - val_sparse_categorical_accuracy: 0.7381
Epoch 47/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 0.9285 - sparse_categorical_accuracy: 0.7199 - val_loss: 0.9787 - val_sparse_categorical_accuracy: 0.7381
Epoch 48/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 0.9754 - sparse_categorical_accuracy: 0.7077 - val_loss: 0.9739 - val_sparse_categorical_accuracy: 0.7381
Epoch 49/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - loss: 0.9472 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9563 - val_sparse_categorical_accuracy: 0.7381
Epoch 50/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 0.9789 - sparse_categorical_accuracy: 0.7206 - val_loss: 0.9652 - val_sparse_categorical_accuracy: 0.7381

Training completed for dataset: RNN-W2V Basic-with best params
Train accuracy: 73.95%
Test accuracy: 73.81%
Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 1s 236ms/step
Confusion Matrix

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step 
              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :                 Data Description Model Description  Train Accuracy  Test Accuracy  F1 Score
0  RNN-W2V Basic-with best params         SimpleRNN           73.95          73.81     62.69


best_model_tfidf, best_history_tfidf, temp_result = train_and_evaluate_model(X_train_tfidf, y_train_tfidf, X_test_tfidf, y_test_tfidf, "RNN on TfIdf Basic with best params")
pd.set_option('display.width',1500)
combinedNNModelResult = pd.concat([combinedNNModelResult, temp_result])

Reloading Tuner from my_dir/hyperparameter_tuning/tuner0.json
Best hyperparameters: {'units': 90, 'dropout_rate1': 0.30000000000000004, 'dropout_rate2': 0.30000000000000004, 'l2_param': 0.0003604061318050688, 'learning_rate': 0.0020246419340776305}
Epoch 1/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 4s 21ms/step - loss: 1.9589 - sparse_categorical_accuracy: 0.2376 - val_loss: 1.4235 - val_sparse_categorical_accuracy: 0.5119
Epoch 2/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - loss: 1.4915 - sparse_categorical_accuracy: 0.4402 - val_loss: 1.0948 - val_sparse_categorical_accuracy: 0.7381
Epoch 3/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - loss: 1.1693 - sparse_categorical_accuracy: 0.6429 - val_loss: 1.0246 - val_sparse_categorical_accuracy: 0.7381
Epoch 4/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - loss: 1.1326 - sparse_categorical_accuracy: 0.7130 - val_loss: 0.9977 - val_sparse_categorical_accuracy: 0.7381
Epoch 5/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - loss: 1.1176 - sparse_categorical_accuracy: 0.6953 - val_loss: 0.9869 - val_sparse_categorical_accuracy: 0.7381
Epoch 6/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - loss: 1.1086 - sparse_categorical_accuracy: 0.6991 - val_loss: 0.9944 - val_sparse_categorical_accuracy: 0.7381
Epoch 7/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - loss: 1.0852 - sparse_categorical_accuracy: 0.7181 - val_loss: 0.9757 - val_sparse_categorical_accuracy: 0.7381
Epoch 8/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 1.0949 - sparse_categorical_accuracy: 0.7128 - val_loss: 0.9842 - val_sparse_categorical_accuracy: 0.7381
Epoch 9/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - loss: 1.0665 - sparse_categorical_accuracy: 0.7081 - val_loss: 0.9924 - val_sparse_categorical_accuracy: 0.7381
Epoch 10/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - loss: 1.0499 - sparse_categorical_accuracy: 0.7152 - val_loss: 1.0179 - val_sparse_categorical_accuracy: 0.7381
Epoch 11/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 1.0660 - sparse_categorical_accuracy: 0.6932 - val_loss: 0.9960 - val_sparse_categorical_accuracy: 0.7381
Epoch 12/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 1.0553 - sparse_categorical_accuracy: 0.7204 - val_loss: 0.9945 - val_sparse_categorical_accuracy: 0.7381
Epoch 13/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.0207 - sparse_categorical_accuracy: 0.7128 - val_loss: 0.9625 - val_sparse_categorical_accuracy: 0.7381
Epoch 14/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.0421 - sparse_categorical_accuracy: 0.7195 - val_loss: 1.0142 - val_sparse_categorical_accuracy: 0.7381
Epoch 15/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 1.0856 - sparse_categorical_accuracy: 0.7202 - val_loss: 0.9934 - val_sparse_categorical_accuracy: 0.7381
Epoch 16/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 1.0498 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9865 - val_sparse_categorical_accuracy: 0.7381
Epoch 17/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - loss: 1.0607 - sparse_categorical_accuracy: 0.7174 - val_loss: 0.9843 - val_sparse_categorical_accuracy: 0.7381
Epoch 18/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 0.9486 - sparse_categorical_accuracy: 0.7191 - val_loss: 0.9840 - val_sparse_categorical_accuracy: 0.7381
Epoch 19/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - loss: 1.0197 - sparse_categorical_accuracy: 0.7185 - val_loss: 0.9627 - val_sparse_categorical_accuracy: 0.7381
Epoch 20/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - loss: 1.0254 - sparse_categorical_accuracy: 0.7143 - val_loss: 1.0093 - val_sparse_categorical_accuracy: 0.7381
Epoch 21/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.9955 - sparse_categorical_accuracy: 0.7213 - val_loss: 1.0496 - val_sparse_categorical_accuracy: 0.7381
Epoch 22/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 1.0045 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9722 - val_sparse_categorical_accuracy: 0.7381
Epoch 23/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9914 - sparse_categorical_accuracy: 0.7206 - val_loss: 0.9643 - val_sparse_categorical_accuracy: 0.7381
Epoch 24/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9892 - sparse_categorical_accuracy: 0.7222 - val_loss: 0.9906 - val_sparse_categorical_accuracy: 0.7381
Epoch 25/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.9876 - sparse_categorical_accuracy: 0.7048 - val_loss: 0.9870 - val_sparse_categorical_accuracy: 0.7381
Epoch 26/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 1.0321 - sparse_categorical_accuracy: 0.7166 - val_loss: 0.9756 - val_sparse_categorical_accuracy: 0.7381
Epoch 27/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: 0.9715 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9752 - val_sparse_categorical_accuracy: 0.7381
Epoch 28/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9938 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9615 - val_sparse_categorical_accuracy: 0.7381
Epoch 29/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - loss: 0.9497 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9662 - val_sparse_categorical_accuracy: 0.7381
Epoch 30/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - loss: 0.9586 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9772 - val_sparse_categorical_accuracy: 0.7381
Epoch 31/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - loss: 0.9556 - sparse_categorical_accuracy: 0.7213 - val_loss: 1.0047 - val_sparse_categorical_accuracy: 0.7381
Epoch 32/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - loss: 1.0062 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9783 - val_sparse_categorical_accuracy: 0.7381
Epoch 33/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - loss: 0.9933 - sparse_categorical_accuracy: 0.7213 - val_loss: 0.9717 - val_sparse_categorical_accuracy: 0.7381
Epoch 34/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - loss: 0.9363 - sparse_categorical_accuracy: 0.7147 - val_loss: 0.9607 - val_sparse_categorical_accuracy: 0.7381
Epoch 35/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.9735 - sparse_categorical_accuracy: 0.7187 - val_loss: 0.9517 - val_sparse_categorical_accuracy: 0.7381
Epoch 36/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9500 - sparse_categorical_accuracy: 0.7213 - val_loss: 1.0095 - val_sparse_categorical_accuracy: 0.7381
Epoch 37/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 0.9317 - sparse_categorical_accuracy: 0.7245 - val_loss: 0.9802 - val_sparse_categorical_accuracy: 0.7381
Epoch 38/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 0.9493 - sparse_categorical_accuracy: 0.7207 - val_loss: 1.0891 - val_sparse_categorical_accuracy: 0.6667
Epoch 39/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - loss: 0.9527 - sparse_categorical_accuracy: 0.7188 - val_loss: 1.0673 - val_sparse_categorical_accuracy: 0.7143
Epoch 40/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.9391 - sparse_categorical_accuracy: 0.7238 - val_loss: 1.0056 - val_sparse_categorical_accuracy: 0.7381
Epoch 41/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9309 - sparse_categorical_accuracy: 0.7222 - val_loss: 0.9677 - val_sparse_categorical_accuracy: 0.7381
Epoch 42/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - loss: 0.9372 - sparse_categorical_accuracy: 0.7213 - val_loss: 1.0057 - val_sparse_categorical_accuracy: 0.7381
Epoch 43/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.9259 - sparse_categorical_accuracy: 0.7291 - val_loss: 0.9360 - val_sparse_categorical_accuracy: 0.7381
Epoch 44/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.9174 - sparse_categorical_accuracy: 0.7212 - val_loss: 1.0145 - val_sparse_categorical_accuracy: 0.7381
Epoch 45/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9332 - sparse_categorical_accuracy: 0.7184 - val_loss: 1.0027 - val_sparse_categorical_accuracy: 0.7381
Epoch 46/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - loss: 0.9112 - sparse_categorical_accuracy: 0.7327 - val_loss: 1.0246 - val_sparse_categorical_accuracy: 0.7262
Epoch 47/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.9158 - sparse_categorical_accuracy: 0.7336 - val_loss: 1.0284 - val_sparse_categorical_accuracy: 0.7381
Epoch 48/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - loss: 0.9511 - sparse_categorical_accuracy: 0.7272 - val_loss: 1.0352 - val_sparse_categorical_accuracy: 0.7381
Epoch 49/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - loss: 0.8683 - sparse_categorical_accuracy: 0.7257 - val_loss: 0.9804 - val_sparse_categorical_accuracy: 0.7381
Epoch 50/50
42/42 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - loss: 0.8809 - sparse_categorical_accuracy: 0.7281 - val_loss: 0.9496 - val_sparse_categorical_accuracy: 0.7381

Training completed for dataset: RNN on TfIdf Basic with best params
Train accuracy: 73.95%
Test accuracy: 73.81%
Train and Validation Loss

Train and Validation Accuracy

3/3 ━━━━━━━━━━━━━━━━━━━━ 1s 230ms/step
Confusion Matrix

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step 
              precision    recall  f1-score   support

           0       0.74      1.00      0.85        62
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        84
   macro avg       0.15      0.20      0.17        84
weighted avg       0.54      0.74      0.63        84

Result Summary 
 --------------------- 
 :                      Data Description Model Description  Train Accuracy  Test Accuracy  F1 Score
0  RNN on TfIdf Basic with best params         SimpleRNN           73.95          73.81     62.69


pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Collating Results : 
-----------------------
                      Data Description                                  Model Description  Train Accuracy  Test Accuracy   F1 Score
0                            W2V Basic                           Basic Model with 1 layer           78.44          75.00  65.160000
0                     Glove Basic Data                           Basic Model with 1 layer           91.02          73.81  69.070000
0                     TFIDF Basic Data                           Basic Model with 1 layer           94.01          70.24  61.330000
0                       W2V Basic Data    NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81  62.690000
0                     Glove Basic data    NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81  62.690000
0                     TFIDF Basic data    NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81  62.690000
0                       W2V Basic Data   NN Classifier Layers-Dropouts-Normalization-ADAM           73.95          71.43  61.940000
0                     Glove Basic Data   NN Classifier Layers-Dropouts-Normalization-ADAM           74.25          73.81  62.690000
0                     TFIDF Basic Data   NN Classifier Layers-Dropouts-Normalization-ADAM           74.55          73.81  62.690000
0                            W2V Basic            Basic w2v NN model with best parameters           73.95          73.81  62.690000
0                          TFIDF Basic  NN Classifier Model Basic TFIDF with best para...           73.95          73.81  62.690000
0                            W2V Basic                     RNN Classifier Basic W2V model           73.95          73.81  62.690000
0                          TFIDF Basic                   RNN Classifier Basic TFIDF model           73.95          73.81  62.690000
0       RNN-W2V Basic-with best params                                          SimpleRNN           73.95          73.81   0.626875
0  RNN on TfIdf Basic with best params                                          SimpleRNN           73.95          73.81   0.626875
0              LSTM on Word2Vec-Basic                                             LSTM NN           71.26          64.29   0.581856
0                  LSTM on tfidf Basic                                            LSTM NN           79.94          72.62   0.642452
0      LSTM-W2V Basic with best params                                           Dense NN           73.95          73.81  62.690000
0       RNN-W2V Basic-with best params                                          SimpleRNN           73.95          73.81  62.690000
0  RNN on TfIdf Basic with best params                                          SimpleRNN           73.95          73.81  62.690000


import random, re
import time
import warnings
warnings.filterwarnings("ignore")

# nlp libraries
import nltk
nltk.download('punkt')
nltk.download('stopwords')

from nltk.tokenize import word_tokenize
from tqdm import tqdm
from nltk.corpus import stopwords
stop_words = stopwords.words('english')
from sklearn.feature_extraction.text import TfidfVectorizer

from wordcloud import WordCloud

from gensim.models import Word2Vec
from gensim.models import KeyedVectors
from gensim.scripts.glove2word2vec import glove2word2vec

from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

pd.options.display.max_columns = None
pd.options.display.max_rows = None

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!


data_lstm = pd.read_excel('drive/My Drive/AIML/Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx')


data_lstm.drop_duplicates(inplace=True)
data_lstm=data_lstm.reset_index(drop=True)
data_lstm.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 425 entries, 0 to 424
Data columns (total 11 columns):
 #   Column                    Non-Null Count  Dtype         
---  ------                    --------------  -----         
 0   Unnamed: 0                425 non-null    int64         
 1   Data                      425 non-null    datetime64[ns]
 2   Countries                 425 non-null    object        
 3   Local                     425 non-null    object        
 4   Industry Sector           425 non-null    object        
 5   Accident Level            425 non-null    object        
 6   Potential Accident Level  425 non-null    object        
 7   Genre                     425 non-null    object        
 8   Employee or Third Party   425 non-null    object        
 9   Critical Risk             425 non-null    object        
 10  Description               425 non-null    object        
dtypes: datetime64[ns](1), int64(1), object(9)
memory usage: 36.6+ KB


# to use regular expressions for manipulating text data
import re

# to load the natural language toolkit
import nltk
nltk.download('stopwords')    # loading the stopwords
nltk.download('wordnet')    # loading the wordnet module that is used in stemming

# to remove common stop words
from nltk.corpus import stopwords

# to perform stemming
from nltk.stem.porter import PorterStemmer

# to create Bag of Words
from sklearn.feature_extraction.text import CountVectorizer

# to import Word2Vec
from gensim.models import Word2Vec

# to display full text in dataframe
pd.set_option('display.max_colwidth', None)

#to tokenize
import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize

# to Lemmetize
from nltk.stem import WordNetLemmatizer
nltk.download("omw-1.4")

[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...

True


#copying to dataframe for nlp processing
df_nlp=data_lstm.copy()


# defining a function to remove special characters
def remove_special_characters(text):
    # Defining the regex pattern to match non-alphanumeric characters
    pattern = '[^A-Za-z0-9]+'

    # Finding the specified pattern and replacing non-alphanumeric characters with a blank string
    new_text = ''.join(re.sub(pattern, ' ', text))

    return new_text


# Applying the function to remove special characters
df_nlp['cleaned_text'] = data_lstm['Description'].apply(remove_special_characters)
# Changing test to lower case
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].str.lower()
# Removing extra whitespaces from the text
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].str.strip()
# Removing extra whitespaces from the text
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].replace(to_replace=r'\d', value='', regex=True)


# Defining a function to remove stop words (like pronouns) using the NLTK library
def remove_stopwords(text):
    # Split text into separate words
    words = text.split()

    # Removing English language stopwords
    new_text = ' '.join([word for word in words if word not in stopwords.words('english')])

    return new_text


# Applying the function to remove stop words using the NLTK library
df_nlp['cleaned_text_without_stopwords'] = df_nlp['cleaned_text'].apply(remove_stopwords)
#Tokenize sentence into words
df_nlp['cleaned_text_stopwords_token'] = df_nlp['cleaned_text_without_stopwords'].apply(word_tokenize)


# Initializing Lemmatizer
wnl = WordNetLemmatizer()

def apply_lemmetizer(text):

    for word in text:
      new_text = ' '.join([wnl.lemmatize(word) for word in text])

    return new_text


# Applying the function to perform lemmetization
df_nlp['final_cleaned_text'] = df_nlp['cleaned_text_stopwords_token'].apply(apply_lemmetizer)


#W2V
# creating a list of all words in our data
words_list = [item.split(" ") for item in df_nlp['final_cleaned_text'].values]


from tensorflow.keras.preprocessing.text import Tokenizer

# Assuming words_list is your list of sentences (a list of lists of words)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(words_list)

# Get the word index from the tokenizer
word_index = tokenizer.word_index

sequences = tokenizer.texts_to_sequences(words_list)

# Calculate sentence lengths
sentence_lengths = [len(seq) for seq in sequences]
max_sentence_length = max(sentence_lengths)


word_index

{'employee': 1,
 'hand': 2,
 'causing': 3,
 'left': 4,
 'right': 5,
 'operator': 6,
 'activity': 7,
 'time': 8,
 'injury': 9,
 'moment': 10,
 'worker': 11,
 'collaborator': 12,
 'area': 13,
 'one': 14,
 'work': 15,
 'equipment': 16,
 'finger': 17,
 'assistant': 18,
 'accident': 19,
 'pipe': 20,
 'level': 21,
 'hit': 22,
 'floor': 23,
 'support': 24,
 'mesh': 25,
 'rock': 26,
 'fall': 27,
 'cm': 28,
 'mr': 29,
 'safety': 30,
 'glove': 31,
 'meter': 32,
 'approximately': 33,
 'team': 34,
 'height': 35,
 'part': 36,
 'side': 37,
 'described': 38,
 'circumstance': 39,
 'cut': 40,
 'pump': 41,
 'kg': 42,
 'truck': 43,
 'injured': 44,
 'face': 45,
 'metal': 46,
 'used': 47,
 'x': 48,
 'place': 49,
 'towards': 50,
 'medical': 51,
 'mechanic': 52,
 'performing': 53,
 'foot': 54,
 'two': 55,
 'using': 56,
 'point': 57,
 'platform': 58,
 'made': 59,
 'remove': 60,
 'end': 61,
 'performed': 62,
 'generating': 63,
 'access': 64,
 'maintenance': 65,
 'fragment': 66,
 'inside': 67,
 'plate': 68,
 'drill': 69,
 'cleaning': 70,
 'back': 71,
 'front': 72,
 'structure': 73,
 'arm': 74,
 'bolt': 75,
 'company': 76,
 'position': 77,
 'hitting': 78,
 'report': 79,
 'water': 80,
 'leg': 81,
 'vehicle': 82,
 'weight': 83,
 'moving': 84,
 'material': 85,
 'car': 86,
 'drilling': 87,
 'use': 88,
 'hose': 89,
 'due': 90,
 'carried': 91,
 'hopper': 92,
 'bar': 93,
 'key': 94,
 'belt': 95,
 'center': 96,
 'piece': 97,
 'impacting': 98,
 'released': 99,
 'block': 100,
 'bite': 101,
 'slide': 102,
 'immediately': 103,
 'ground': 104,
 'first': 105,
 'came': 106,
 'wearing': 107,
 'rod': 108,
 'nv': 109,
 'head': 110,
 'caused': 111,
 'small': 112,
 'technician': 113,
 'event': 114,
 'upper': 115,
 'moved': 116,
 'gable': 117,
 'edge': 118,
 'trying': 119,
 'sting': 120,
 'eye': 121,
 'ladder': 122,
 'make': 123,
 'contact': 124,
 'tube': 125,
 'helmet': 126,
 'impact': 127,
 'line': 128,
 'another': 129,
 'reaching': 130,
 'falling': 131,
 'projected': 132,
 'loading': 133,
 'approx': 134,
 'move': 135,
 'movement': 136,
 'near': 137,
 'base': 138,
 'da': 139,
 'silva': 140,
 'neck': 141,
 'diameter': 142,
 'bee': 143,
 'cutting': 144,
 'pressure': 145,
 'second': 146,
 'slip': 147,
 'loader': 148,
 'grating': 149,
 'boot': 150,
 'step': 151,
 'glass': 152,
 'lower': 153,
 'person': 154,
 'reaction': 155,
 'slipped': 156,
 'wound': 157,
 'acid': 158,
 'producing': 159,
 'air': 160,
 'region': 161,
 'hammer': 162,
 'top': 163,
 'return': 164,
 'sheet': 165,
 'type': 166,
 'field': 167,
 'allergic': 168,
 'removing': 169,
 'personnel': 170,
 'pulley': 171,
 'without': 172,
 'go': 173,
 'n': 174,
 'burn': 175,
 'opening': 176,
 'cable': 177,
 'locomotive': 178,
 'pressed': 179,
 'driver': 180,
 'chain': 181,
 'workshop': 182,
 'collection': 183,
 'took': 184,
 'removal': 185,
 'located': 186,
 'steel': 187,
 'machine': 188,
 'direction': 189,
 'p': 190,
 'ore': 191,
 'frame': 192,
 'guard': 193,
 'cathode': 194,
 'open': 195,
 'release': 196,
 'control': 197,
 'placed': 198,
 'scissor': 199,
 'pressing': 200,
 'box': 201,
 'inspection': 202,
 'returned': 203,
 'hydraulic': 204,
 'tank': 205,
 'hole': 206,
 'partner': 207,
 'last': 208,
 'away': 209,
 'fell': 210,
 'slight': 211,
 'hour': 212,
 'feel': 213,
 'pain': 214,
 'bag': 215,
 'maneuver': 216,
 'turn': 217,
 'mining': 218,
 'verify': 219,
 'lifting': 220,
 'making': 221,
 'gate': 222,
 'tower': 223,
 'suddenly': 224,
 'help': 225,
 'unit': 226,
 'knee': 227,
 'stop': 228,
 'basket': 229,
 'soil': 230,
 'behind': 231,
 'carrying': 232,
 'wall': 233,
 'lip': 234,
 'projection': 235,
 'proceeds': 236,
 'solution': 237,
 'tool': 238,
 'come': 239,
 'holding': 240,
 'plant': 241,
 'instant': 242,
 'door': 243,
 'order': 244,
 'room': 245,
 'master': 246,
 'roof': 247,
 'drainage': 248,
 'positioned': 249,
 'held': 250,
 'op': 251,
 'change': 252,
 'stopped': 253,
 'forearm': 254,
 'site': 255,
 'test': 256,
 'ring': 257,
 'welder': 258,
 'mine': 259,
 'concrete': 260,
 'load': 261,
 'referred': 262,
 'attacked': 263,
 'cx': 264,
 'auxiliary': 265,
 'leaving': 266,
 'suffered': 267,
 'went': 268,
 'section': 269,
 'preparing': 270,
 'set': 271,
 'zinc': 272,
 'lift': 273,
 'container': 274,
 'lens': 275,
 'removed': 276,
 'cylinder': 277,
 'blade': 278,
 'ramp': 279,
 'balance': 280,
 'flange': 281,
 'crown': 282,
 'wooden': 283,
 'road': 284,
 'pm': 285,
 'h': 286,
 'lesion': 287,
 'th': 288,
 'surface': 289,
 'scoop': 290,
 'struck': 291,
 'around': 292,
 'blow': 293,
 'staff': 294,
 'victim': 295,
 'swelling': 296,
 'supervisor': 297,
 'hospital': 298,
 'detached': 299,
 'split': 300,
 'shoulder': 301,
 'transferred': 302,
 'pulling': 303,
 'break': 304,
 'presence': 305,
 'take': 306,
 'shotcrete': 307,
 'de': 308,
 'thumb': 309,
 'system': 310,
 'valve': 311,
 'cabin': 312,
 'third': 313,
 'stepped': 314,
 'iron': 315,
 'sleeve': 316,
 'superficial': 317,
 'service': 318,
 'manoel': 319,
 'strip': 320,
 'mud': 321,
 'jumbo': 322,
 'pull': 323,
 'beam': 324,
 'tip': 325,
 'coming': 326,
 'lever': 327,
 'operation': 328,
 'positioning': 329,
 'board': 330,
 'surprised': 331,
 'rope': 332,
 'body': 333,
 'generates': 334,
 'force': 335,
 'enter': 336,
 'chute': 337,
 'leather': 338,
 'get': 339,
 'little': 340,
 'notice': 341,
 'liquid': 342,
 'evacuated': 343,
 'start': 344,
 'minor': 345,
 'mx': 346,
 'middle': 347,
 'bolter': 348,
 'close': 349,
 'untimely': 350,
 'hooked': 351,
 'wire': 352,
 'geological': 353,
 'found': 354,
 'tire': 355,
 'forest': 356,
 'continued': 357,
 'project': 358,
 'gps': 359,
 'noise': 360,
 'easel': 361,
 'flow': 362,
 'reach': 363,
 'task': 364,
 'length': 365,
 'high': 366,
 'verified': 367,
 'suffering': 368,
 'day': 369,
 'hot': 370,
 'post': 371,
 'evaluation': 372,
 'electric': 373,
 'product': 374,
 'bearing': 375,
 'mechanical': 376,
 'ventilation': 377,
 'chimney': 378,
 'probe': 379,
 'mixkret': 380,
 'rim': 381,
 'pit': 382,
 'ear': 383,
 'would': 384,
 'onto': 385,
 'central': 386,
 'broken': 387,
 'toward': 388,
 'upon': 389,
 'covered': 390,
 'taken': 391,
 'suction': 392,
 'chin': 393,
 'rubber': 394,
 'inch': 395,
 'fifth': 396,
 'evaluate': 397,
 'continue': 398,
 'discomfort': 399,
 'pulp': 400,
 'sample': 401,
 'normal': 402,
 'normally': 403,
 'completed': 404,
 'give': 405,
 'quickly': 406,
 'following': 407,
 'city': 408,
 'epps': 409,
 'e': 410,
 'later': 411,
 'climb': 412,
 'three': 413,
 'discharge': 414,
 'projecting': 415,
 'irritation': 416,
 'placing': 417,
 'proceeded': 418,
 'manually': 419,
 'electrician': 420,
 'manual': 421,
 'forehead': 422,
 'g': 423,
 'conveyor': 424,
 'ppe': 425,
 'passed': 426,
 'scaffolding': 427,
 'striking': 428,
 'handle': 429,
 'chamber': 430,
 'table': 431,
 'attention': 432,
 'decides': 433,
 'lid': 434,
 'bp': 435,
 'imprisoned': 436,
 'check': 437,
 'care': 438,
 'press': 439,
 'process': 440,
 'next': 441,
 'rotation': 442,
 'index': 443,
 'protection': 444,
 'ankle': 445,
 'cat': 446,
 'cloth': 447,
 'pass': 448,
 'returning': 449,
 'washing': 450,
 'gutter': 451,
 'decide': 452,
 'ingot': 453,
 'clean': 454,
 'st': 455,
 'entrance': 456,
 'inner': 457,
 'clerk': 458,
 'bucket': 459,
 'suffers': 460,
 'performs': 461,
 'target': 462,
 'accompanied': 463,
 'south': 464,
 'avoid': 465,
 'geologist': 466,
 'push': 467,
 'filling': 468,
 'entering': 469,
 'well': 470,
 'evacuation': 471,
 'local': 472,
 'leave': 473,
 'emergency': 474,
 'palm': 475,
 'proceed': 476,
 'unloading': 477,
 'mouth': 478,
 'displacement': 479,
 'aid': 480,
 'realized': 481,
 'opened': 482,
 'try': 483,
 'installation': 484,
 'lock': 485,
 'filter': 486,
 'passing': 487,
 'thermal': 488,
 'shock': 489,
 'splash': 490,
 'cell': 491,
 'degree': 492,
 'crane': 493,
 'occurred': 494,
 'stuck': 495,
 'hold': 496,
 'communicates': 497,
 'gr': 498,
 'mill': 499,
 'entered': 500,
 'ampoloader': 501,
 'rear': 502,
 'holder': 503,
 'withdrawal': 504,
 'nd': 505,
 'turned': 506,
 'find': 507,
 'mineral': 508,
 'noticing': 509,
 'oil': 510,
 'cover': 511,
 'loses': 512,
 'suspended': 513,
 'boiler': 514,
 'waste': 515,
 'leaf': 516,
 'rail': 517,
 'ahead': 518,
 'traveled': 519,
 'bruise': 520,
 'metallic': 521,
 'fracture': 522,
 'pin': 523,
 'enters': 524,
 'sound': 525,
 'approximate': 526,
 'stilson': 527,
 'heading': 528,
 'way': 529,
 'zone': 530,
 'deep': 531,
 'rub': 532,
 'together': 533,
 'driller': 534,
 'could': 535,
 'stump': 536,
 'wood': 537,
 'loose': 538,
 'tubing': 539,
 'contusion': 540,
 'strap': 541,
 'internal': 542,
 'traveling': 543,
 'b': 544,
 'paralyzed': 545,
 'engine': 546,
 'electrical': 547,
 'affected': 548,
 'bank': 549,
 'complete': 550,
 'vegetation': 551,
 'received': 552,
 'parked': 553,
 'tipper': 554,
 'fourth': 555,
 'execution': 556,
 'bitten': 557,
 'particle': 558,
 'branch': 559,
 'positive': 560,
 'c': 561,
 'preparation': 562,
 'light': 563,
 'felt': 564,
 'rafael': 565,
 'danillo': 566,
 'uniform': 567,
 'robson': 568,
 'maid': 569,
 'doctor': 570,
 'station': 571,
 'pick': 572,
 'ob': 573,
 'begin': 574,
 'unlocking': 575,
 'impromec': 576,
 'distance': 577,
 'transmission': 578,
 'tension': 579,
 'resting': 580,
 'protective': 581,
 'winch': 582,
 'lunch': 583,
 'working': 584,
 'intersection': 585,
 'phase': 586,
 'perforation': 587,
 'wheel': 588,
 'heated': 589,
 'chisel': 590,
 'track': 591,
 'clinic': 592,
 'power': 593,
 'cheekbone': 594,
 'pushing': 595,
 'still': 596,
 'fire': 597,
 'wrench': 598,
 'hdpe': 599,
 'blocked': 600,
 'people': 601,
 'impacted': 602,
 'protruding': 603,
 'mt': 604,
 'oven': 605,
 'lamp': 606,
 'cleaned': 607,
 'window': 608,
 'epp': 609,
 'alone': 610,
 'sustained': 611,
 'helper': 612,
 'descending': 613,
 'blunt': 614,
 'nut': 615,
 'clamp': 616,
 'pvc': 617,
 'imprisons': 618,
 'general': 619,
 'slope': 620,
 'sole': 621,
 'distal': 622,
 'handling': 623,
 'long': 624,
 'tail': 625,
 'incident': 626,
 'main': 627,
 'carry': 628,
 'perform': 629,
 'roll': 630,
 'welding': 631,
 'initial': 632,
 'ustulation': 633,
 'mapping': 634,
 'necessary': 635,
 'house': 636,
 'space': 637,
 'put': 638,
 'drop': 639,
 'happened': 640,
 'saw': 641,
 'wrist': 642,
 'allergy': 643,
 'needed': 644,
 'attack': 645,
 'previously': 646,
 'lt': 647,
 'convoy': 648,
 'treated': 649,
 'stage': 650,
 'nio': 651,
 'several': 652,
 'cmxcmxcm': 653,
 'see': 654,
 'thorn': 655,
 'decided': 656,
 'overflow': 657,
 'walking': 658,
 'attempt': 659,
 'drawer': 660,
 'chest': 661,
 'assembly': 662,
 'dust': 663,
 'hat': 664,
 'protector': 665,
 'fence': 666,
 'extension': 667,
 'bumped': 668,
 'full': 669,
 'sampling': 670,
 'loud': 671,
 'old': 672,
 'loosen': 673,
 'directed': 674,
 'note': 675,
 'identified': 676,
 'proceeding': 677,
 'motor': 678,
 'raise': 679,
 'forklift': 680,
 'big': 681,
 'installing': 682,
 'xxcm': 683,
 'channel': 684,
 'housing': 685,
 'pot': 686,
 'backwards': 687,
 'accumulation': 688,
 'indicated': 689,
 'jhonatan': 690,
 'nilton': 691,
 'hq': 692,
 'standing': 693,
 'bottom': 694,
 'rest': 695,
 'finished': 696,
 'cocada': 697,
 'limb': 698,
 'asks': 699,
 'started': 700,
 'clearing': 701,
 'tie': 702,
 'coil': 703,
 'warehouse': 704,
 'seat': 705,
 'apparently': 706,
 'blowing': 707,
 'official': 708,
 'patrol': 709,
 'remained': 710,
 'furnace': 711,
 'incimmet': 712,
 'explosive': 713,
 'anfoloader': 714,
 'throwing': 715,
 'jump': 716,
 'cause': 717,
 'cabinet': 718,
 'activates': 719,
 'arranged': 720,
 'cart': 721,
 'accessory': 722,
 'vertical': 723,
 'form': 724,
 'adjustment': 725,
 'william': 726,
 'supported': 727,
 'twist': 728,
 'free': 729,
 'bend': 730,
 'radius': 731,
 'approaching': 732,
 'supervising': 733,
 'previous': 734,
 'hdp': 735,
 'four': 736,
 'fine': 737,
 'transfer': 738,
 'grid': 739,
 'bomb': 740,
 'across': 741,
 'machete': 742,
 'locking': 743,
 'strike': 744,
 'trip': 745,
 'anchor': 746,
 'lifted': 747,
 'cap': 748,
 'empty': 749,
 'store': 750,
 'gallery': 751,
 'us': 752,
 'verifying': 753,
 'remaining': 754,
 'wick': 755,
 'pole': 756,
 'chuck': 757,
 'climbing': 758,
 'sulfuric': 759,
 'observes': 760,
 'recovery': 761,
 'electrolysis': 762,
 'feeder': 763,
 'occurs': 764,
 'pilot': 765,
 'co': 766,
 'connection': 767,
 'composed': 768,
 'divine': 769,
 'staircase': 770,
 'forward': 771,
 'member': 772,
 'exit': 773,
 'reduced': 774,
 'got': 775,
 'getting': 776,
 'radio': 777,
 'followed': 778,
 'leak': 779,
 'closing': 780,
 'soon': 781,
 'involved': 782,
 'pad': 783,
 'scooptram': 784,
 'reducer': 785,
 'pasco': 786,
 'lane': 787,
 'cone': 788,
 'shank': 789,
 'corrugated': 790,
 'twisting': 791,
 'survey': 792,
 'dining': 793,
 'anode': 794,
 'closed': 795,
 'office': 796,
 'advance': 797,
 'rebound': 798,
 'railing': 799,
 'chuteo': 800,
 'gun': 801,
 'turning': 802,
 'fan': 803,
 'routine': 804,
 'shape': 805,
 'supporting': 806,
 'license': 807,
 'fabio': 808,
 'screen': 809,
 'wasp': 810,
 'fenced': 811,
 'heard': 812,
 'intermediate': 813,
 'sub': 814,
 'excavation': 815,
 'bounce': 816,
 'shoe': 817,
 'metatarsal': 818,
 'prevent': 819,
 'locked': 820,
 'instep': 821,
 'ustulado': 822,
 'need': 823,
 'unclog': 824,
 'procedure': 825,
 'unexpectedly': 826,
 'grab': 827,
 'excoriation': 828,
 'weighing': 829,
 'rice': 830,
 'including': 831,
 'residual': 832,
 'tried': 833,
 'flash': 834,
 'jacket': 835,
 'increase': 836,
 'row': 837,
 'thigh': 838,
 'called': 839,
 'warning': 840,
 'concentrate': 841,
 'r': 842,
 'assisted': 843,
 'horizontally': 844,
 'releasing': 845,
 'effect': 846,
 'exerted': 847,
 'entry': 848,
 'tilted': 849,
 'aluminum': 850,
 'short': 851,
 'walk': 852,
 'fact': 853,
 'natclar': 854,
 'secondary': 855,
 'effort': 856,
 'engineer': 857,
 'amount': 858,
 'wore': 859,
 'embedded': 860,
 'alpha': 861,
 'finding': 862,
 'noticed': 863,
 'la': 864,
 'telescopic': 865,
 'storm': 866,
 'pumping': 867,
 'low': 868,
 'lose': 869,
 'starting': 870,
 'realizing': 871,
 'stung': 872,
 'leakage': 873,
 'transporting': 874,
 'unbalanced': 875,
 'leaching': 876,
 'cement': 877,
 'slid': 878,
 'action': 879,
 'maperu': 880,
 'wagon': 881,
 'radial': 882,
 'negative': 883,
 'bit': 884,
 'coupling': 885,
 'abruptly': 886,
 'mobile': 887,
 'hook': 888,
 'hoist': 889,
 'going': 890,
 'ripper': 891,
 'mooring': 892,
 'blasting': 893,
 'cutter': 894,
 'worked': 895,
 'cross': 896,
 'sling': 897,
 'reached': 898,
 'electrowelded': 899,
 'shear': 900,
 'secured': 901,
 'raised': 902,
 'square': 903,
 'fixing': 904,
 'heavy': 905,
 'supply': 906,
 'sliding': 907,
 'thickener': 908,
 'nose': 909,
 'manipulating': 910,
 'parking': 911,
 'brake': 912,
 'safe': 913,
 'conditioning': 914,
 'shifted': 915,
 'quirodactyl': 916,
 'identify': 917,
 'since': 918,
 'able': 919,
 'phalanx': 920,
 'scaffold': 921,
 'van': 922,
 'nail': 923,
 'stumble': 924,
 'rb': 925,
 'ton': 926,
 'residue': 927,
 'helical': 928,
 'tell': 929,
 'resulting': 930,
 'slightly': 931,
 'l': 932,
 'angle': 933,
 'stool': 934,
 'sink': 935,
 'pierced': 936,
 'receiving': 937,
 'additive': 938,
 'thickness': 939,
 'align': 940,
 'originating': 941,
 'operates': 942,
 'polyethylene': 943,
 'steam': 944,
 'disk': 945,
 'strut': 946,
 'burning': 947,
 'transport': 948,
 'inspect': 949,
 'along': 950,
 'mask': 951,
 'according': 952,
 'interior': 953,
 'assembling': 954,
 'accommodate': 955,
 'fuel': 956,
 'stone': 957,
 'paracatu': 958,
 'orlando': 959,
 'boltec': 960,
 'plug': 961,
 'correct': 962,
 'arc': 963,
 'cord': 964,
 'coordinated': 965,
 'prospector': 966,
 'already': 967,
 'reconnaissance': 968,
 'felipe': 969,
 'encountered': 970,
 'leaning': 971,
 'sediment': 972,
 'current': 973,
 'wca': 974,
 'ran': 975,
 'guide': 976,
 'state': 977,
 'highway': 978,
 'aripuan': 979,
 'distancing': 980,
 'hurried': 981,
 'girdle': 982,
 'goggles': 983,
 'paulo': 984,
 'autoclave': 985,
 'problem': 986,
 'camera': 987,
 'km': 988,
 'respective': 989,
 'dropped': 990,
 'external': 991,
 'treatment': 992,
 'rupture': 993,
 'mean': 994,
 'knife': 995,
 'compressed': 996,
 'fixed': 997,
 'throw': 998,
 'squat': 999,
 'cervical': 1000,
 ...}


from tensorflow.keras.preprocessing.sequence import pad_sequences

# Pad sequences to ensure uniform length
X_padded = pad_sequences(sequences, maxlen=max_sentence_length)

print("Padded sequences shape:", X_padded.shape)

Padded sequences shape: (425, 95)


# creating an instance of Word2Vec
model_W2V = Word2Vec(words_list, vector_size = 100, min_count = 1, window=5, workers = 6)
# Checking the size of the vocabulary
print("Length of the vocabulary is", len(list(model_W2V.wv.key_to_index)))

Length of the vocabulary is 2800


# Dictionary with key as words and the value as the embedding vector.

words = list(model_W2V.wv.key_to_index.keys())

# Retrieving word vectors for all the words present in the model's vocabulary
wvs = model_W2V.wv[words].tolist()

# Creating a dictionary of words and their corresponding vectors
word_vector_dict = dict(zip(words, wvs))


word_vector_dict

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import numpy as np

# Define the size of the embedding
embedding_dim = 100  # This should match the vector_size used in Word2Vec
vocab_size = len(word_index) + 1  # +1 because Keras word indices are 1-based

# Initialize embedding matrix with zeros
embedding_matrix = np.zeros((vocab_size, embedding_dim))

# Fill the embedding matrix with Word2Vec vectors
for word, i in word_index.items():
    if word in word_vector_dict:  # Check if the word exists in the Word2Vec model
        embedding_matrix[i] = word_vector_dict[word]


embedding_matrix

array([[ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ],
       [-0.01521955,  0.05100543,  0.01691782, ..., -0.0638089 ,
         0.0146303 , -0.00199482],
       [-0.02627162,  0.05644401,  0.01757281, ..., -0.06093726,
         0.00414692, -0.00294113],
       ...,
       [ 0.00115309,  0.00894521, -0.00145534, ...,  0.0082667 ,
         0.00423198,  0.00361376],
       [-0.00211785,  0.00153961, -0.00767346, ..., -0.00041441,
        -0.00892128, -0.00603138],
       [-0.00865966,  0.00694644, -0.00450014, ..., -0.00691126,
         0.00505519,  0.00631498]])


print (vocab_size)
print (embedding_matrix.shape)
print (max_sentence_length)
print (embedding_dim)

2801
(2801, 100)
95
100


y = data_lstm['Accident Level']


!pip install scikit-learn  # Install scikit-learn if you haven't already
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse_output=False)  # Create OneHotEncoder instance
y_encoded = encoder.fit_transform(y.values.reshape(-1, 1))  # Reshape and encode

Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.5.1)
Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)
Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)
Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)
Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)


X_train, X_test, y_train, y_test = train_test_split(X_padded, y_encoded, test_size=0.2,stratify=y, random_state=42)


from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout

model = Sequential()

# Create the embedding layer
embedding_layer = Embedding(input_dim=vocab_size,
                            output_dim=embedding_dim,
                            weights=[embedding_matrix],
                            input_length=max_sentence_length,  # sentence_length is the length of input sequences
                            trainable=False)  # Set trainable to False to use the embeddings as they are
model.add(embedding_layer)

# Add other layers (LSTM, Dense, etc.)
model.add(LSTM(100))
model.add(Dropout(0.3))
model.add(Dense(5, activation='softmax'))

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.summary()

Model: "sequential_5"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ embedding (Embedding)                │ ?                           │         280,100 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_4 (LSTM)                        │ ?                           │     0 (unbuilt) │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_10 (Dropout)                 │ ?                           │     0 (unbuilt) │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_7 (Dense)                      │ ?                           │     0 (unbuilt) │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 280,100 (1.07 MB)

 Trainable params: 0 (0.00 B)

 Non-trainable params: 280,100 (1.07 MB)


model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))

Epoch 1/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 9s 272ms/step - accuracy: 0.6070 - loss: 1.5298 - val_accuracy: 0.7412 - val_loss: 1.0146
Epoch 2/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 2s 148ms/step - accuracy: 0.7560 - loss: 0.9501 - val_accuracy: 0.7412 - val_loss: 0.9845
Epoch 3/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 2s 138ms/step - accuracy: 0.7560 - loss: 0.9215 - val_accuracy: 0.7412 - val_loss: 0.9299
Epoch 4/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 84ms/step - accuracy: 0.7560 - loss: 0.9039 - val_accuracy: 0.7412 - val_loss: 0.9246
Epoch 5/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 79ms/step - accuracy: 0.7560 - loss: 0.8815 - val_accuracy: 0.7412 - val_loss: 0.9214
Epoch 6/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 78ms/step - accuracy: 0.7560 - loss: 0.8767 - val_accuracy: 0.7412 - val_loss: 0.9192
Epoch 7/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 85ms/step - accuracy: 0.7560 - loss: 0.8927 - val_accuracy: 0.7412 - val_loss: 0.9171
Epoch 8/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 91ms/step - accuracy: 0.7560 - loss: 0.8800 - val_accuracy: 0.7412 - val_loss: 0.9165
Epoch 9/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 85ms/step - accuracy: 0.7560 - loss: 0.8720 - val_accuracy: 0.7412 - val_loss: 0.9164
Epoch 10/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 92ms/step - accuracy: 0.7560 - loss: 0.8855 - val_accuracy: 0.7412 - val_loss: 0.9148
Epoch 11/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 93ms/step - accuracy: 0.7560 - loss: 0.8699 - val_accuracy: 0.7412 - val_loss: 0.9139
Epoch 12/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 85ms/step - accuracy: 0.7560 - loss: 0.8623 - val_accuracy: 0.7412 - val_loss: 0.9131
Epoch 13/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 126ms/step - accuracy: 0.7560 - loss: 0.8856 - val_accuracy: 0.7412 - val_loss: 0.9124
Epoch 14/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 3s 147ms/step - accuracy: 0.7560 - loss: 0.8642 - val_accuracy: 0.7412 - val_loss: 0.9121
Epoch 15/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 129ms/step - accuracy: 0.7560 - loss: 0.8647 - val_accuracy: 0.7412 - val_loss: 0.9114
Epoch 16/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 88ms/step - accuracy: 0.7560 - loss: 0.8657 - val_accuracy: 0.7412 - val_loss: 0.9111
Epoch 17/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 83ms/step - accuracy: 0.7560 - loss: 0.8637 - val_accuracy: 0.7412 - val_loss: 0.9103
Epoch 18/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 90ms/step - accuracy: 0.7560 - loss: 0.8649 - val_accuracy: 0.7412 - val_loss: 0.9100
Epoch 19/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 94ms/step - accuracy: 0.7560 - loss: 0.8706 - val_accuracy: 0.7412 - val_loss: 0.9096
Epoch 20/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 87ms/step - accuracy: 0.7560 - loss: 0.8678 - val_accuracy: 0.7412 - val_loss: 0.9091
Epoch 21/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 88ms/step - accuracy: 0.7560 - loss: 0.8592 - val_accuracy: 0.7412 - val_loss: 0.9088
Epoch 22/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 87ms/step - accuracy: 0.7560 - loss: 0.8603 - val_accuracy: 0.7412 - val_loss: 0.9082
Epoch 23/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 84ms/step - accuracy: 0.7560 - loss: 0.8516 - val_accuracy: 0.7412 - val_loss: 0.9079
Epoch 24/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 112ms/step - accuracy: 0.7560 - loss: 0.8654 - val_accuracy: 0.7412 - val_loss: 0.9076
Epoch 25/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 2s 136ms/step - accuracy: 0.7560 - loss: 0.8474 - val_accuracy: 0.7412 - val_loss: 0.9074
Epoch 26/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 3s 146ms/step - accuracy: 0.7560 - loss: 0.8572 - val_accuracy: 0.7412 - val_loss: 0.9071
Epoch 27/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 89ms/step - accuracy: 0.7560 - loss: 0.8727 - val_accuracy: 0.7412 - val_loss: 0.9073
Epoch 28/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 87ms/step - accuracy: 0.7560 - loss: 0.8548 - val_accuracy: 0.7412 - val_loss: 0.9073
Epoch 29/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 83ms/step - accuracy: 0.7560 - loss: 0.8664 - val_accuracy: 0.7412 - val_loss: 0.9070
Epoch 30/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 80ms/step - accuracy: 0.7560 - loss: 0.8666 - val_accuracy: 0.7412 - val_loss: 0.9068
Epoch 31/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 82ms/step - accuracy: 0.7560 - loss: 0.8809 - val_accuracy: 0.7412 - val_loss: 0.9068
Epoch 32/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 82ms/step - accuracy: 0.7560 - loss: 0.8539 - val_accuracy: 0.7412 - val_loss: 0.9071
Epoch 33/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 83ms/step - accuracy: 0.7560 - loss: 0.8588 - val_accuracy: 0.7412 - val_loss: 0.9067
Epoch 34/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 85ms/step - accuracy: 0.7560 - loss: 0.8646 - val_accuracy: 0.7412 - val_loss: 0.9065
Epoch 35/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 81ms/step - accuracy: 0.7560 - loss: 0.8649 - val_accuracy: 0.7412 - val_loss: 0.9064
Epoch 36/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 79ms/step - accuracy: 0.7560 - loss: 0.8527 - val_accuracy: 0.7412 - val_loss: 0.9065
Epoch 37/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 2s 134ms/step - accuracy: 0.7560 - loss: 0.8595 - val_accuracy: 0.7412 - val_loss: 0.9067
Epoch 38/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 3s 144ms/step - accuracy: 0.7560 - loss: 0.8555 - val_accuracy: 0.7412 - val_loss: 0.9067
Epoch 39/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 2s 82ms/step - accuracy: 0.7560 - loss: 0.8641 - val_accuracy: 0.7412 - val_loss: 0.9068
Epoch 40/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 81ms/step - accuracy: 0.7560 - loss: 0.8563 - val_accuracy: 0.7412 - val_loss: 0.9067
Epoch 41/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 80ms/step - accuracy: 0.7560 - loss: 0.8505 - val_accuracy: 0.7412 - val_loss: 0.9069
Epoch 42/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 82ms/step - accuracy: 0.7560 - loss: 0.8612 - val_accuracy: 0.7412 - val_loss: 0.9085
Epoch 43/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 86ms/step - accuracy: 0.7560 - loss: 0.8555 - val_accuracy: 0.7412 - val_loss: 0.9081
Epoch 44/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 86ms/step - accuracy: 0.7560 - loss: 0.8621 - val_accuracy: 0.7412 - val_loss: 0.9083
Epoch 45/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 95ms/step - accuracy: 0.7560 - loss: 0.8493 - val_accuracy: 0.7412 - val_loss: 0.9089
Epoch 46/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 79ms/step - accuracy: 0.7560 - loss: 0.8628 - val_accuracy: 0.7412 - val_loss: 0.9114
Epoch 47/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 112ms/step - accuracy: 0.7560 - loss: 0.8570 - val_accuracy: 0.7412 - val_loss: 0.9133
Epoch 48/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 2s 136ms/step - accuracy: 0.7560 - loss: 0.8603 - val_accuracy: 0.7412 - val_loss: 0.9130
Epoch 49/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 3s 149ms/step - accuracy: 0.7560 - loss: 0.8564 - val_accuracy: 0.7412 - val_loss: 0.9138
Epoch 50/50
11/11 ━━━━━━━━━━━━━━━━━━━━ 3s 200ms/step - accuracy: 0.7560 - loss: 0.8366 - val_accuracy: 0.7412 - val_loss: 0.9152

<keras.src.callbacks.history.History at 0x7f71e299bbe0>


test_loss, test_accuracy = model.evaluate(X_test, y_test)
print(f"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}")
train_loss, train_accuracy = model.evaluate(X_train, y_train)
print(f"Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}")

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 43ms/step - accuracy: 0.7729 - loss: 0.8288
Test Loss: 0.9152, Test Accuracy: 0.7412
11/11 ━━━━━━━━━━━━━━━━━━━━ 1s 51ms/step - accuracy: 0.7331 - loss: 0.9073
Train Loss: 0.8707, Train Accuracy: 0.7441


y_pred = model.predict(X_test)
F1_score= f1_score(np.argmax(y_test, axis=1), np.argmax(y_pred, axis=1), average='weighted')
tempResult = pd.DataFrame({'Data Description':"LSTM on W2V ",
                                'Model Description':"LSTM on W2V dataset",
                                'Train Accuracy': round(train_accuracy*100,2),
                                'Test Accuracy':round(test_accuracy*100,2),
                                'F1 Score':round(F1_score*100,2),
                                }, index=[0])

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 52ms/step


cm = confusion_matrix(np.argmax(y_test, axis=1),np.argmax(y_pred, axis=1))
sns.heatmap(cm,
            annot=True,
            fmt='g',
            xticklabels=['I','II','III', 'IV','V'],
            yticklabels=['I','II','III', 'IV','V'])
plt.ylabel('Actual', fontsize=13)
plt.title('Confusion Matrix', fontsize=17, pad=20)
plt.gca().xaxis.set_label_position('top')
plt.xlabel('Prediction', fontsize=13)
plt.gca().xaxis.tick_top()

plt.gca().figure.subplots_adjust(bottom=0.2)
plt.gca().figure.text(0.5, 0.05, 'Prediction', ha='center', fontsize=13)
plt.show()


print(classification_report(np.argmax(y_test, axis=1),np.argmax(y_pred, axis=1)))

              precision    recall  f1-score   support

           0       0.74      1.00      0.85        63
           1       0.00      0.00      0.00         8
           2       0.00      0.00      0.00         6
           3       0.00      0.00      0.00         6
           4       0.00      0.00      0.00         2

    accuracy                           0.74        85
   macro avg       0.15      0.20      0.17        85
weighted avg       0.55      0.74      0.63        85


combinedNNModelResult=pd.concat([combinedNNModelResult, tempResult])


#reset index
combinedNNModelResult.reset_index(drop=True, inplace=True)


pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Collating Results : 
-----------------------
                       Data Description                                     Model Description  Train Accuracy  Test Accuracy  F1 Score
0                             W2V Basic                              Basic Model with 1 layer           78.44          75.00     65.16
1                      Glove Basic Data                              Basic Model with 1 layer           91.02          73.81     69.07
2                      TFIDF Basic Data                              Basic Model with 1 layer           94.01          70.24     61.33
3                        W2V Basic Data       NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
4                      Glove Basic data       NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
5                      TFIDF Basic data       NN Classifier Layers-Dropouts-Normalization-SGD           73.95          73.81     62.69
6                        W2V Basic Data      NN Classifier Layers-Dropouts-Normalization-ADAM           73.95          71.43     61.94
7                      Glove Basic Data      NN Classifier Layers-Dropouts-Normalization-ADAM           74.25          73.81     62.69
8                      TFIDF Basic Data      NN Classifier Layers-Dropouts-Normalization-ADAM           74.55          73.81     62.69
9                             W2V Basic               Basic w2v NN model with best parameters           73.95          73.81     62.69
10                          TFIDF Basic  NN Classifier Model Basic TFIDF with best parameters           73.95          73.81     62.69
11                            W2V Basic                        RNN Classifier Basic W2V model           73.95          73.81     62.69
12                          TFIDF Basic                      RNN Classifier Basic TFIDF model           73.95          73.81     62.69
13       RNN-W2V Basic-with best params                                             SimpleRNN           73.95          73.81     62.69
14  RNN on TfIdf Basic with best params                                             SimpleRNN           73.95          73.81     62.69
15                         LSTM on W2V                                    LSTM on W2V dataset           74.41          74.12     63.10


combinedNNModelResult


#used for augmentation
pip install nlpaug

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Installing collected packages: nlpaug
Successfully installed nlpaug-1.1.11


!pip install tensorflow_hub==0.16.1
!pip install tf-keras
!pip install tensorflow-text

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import random
import re
import time
import warnings
import pickle
import os
import string

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Embedding, LSTM, Dropout, Bidirectional, SpatialDropout1D, GRU
from tensorflow.keras.optimizers import SGD, Adam
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.sequence import pad_sequences

from sklearn.preprocessing import StandardScaler, PowerTransformer, LabelEncoder, OneHotEncoder
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, KFold, LeaveOneOut, StratifiedKFold, RepeatedStratifiedKFold
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, BaggingClassifier, AdaBoostClassifier
from sklearn.linear_model import RidgeClassifier, LogisticRegression, Lasso
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.metrics import classification_report, accuracy_score, f1_score, confusion_matrix, recall_score, precision_score, mean_squared_error, r2_score
from sklearn.utils import resample
from imblearn.over_sampling import SMOTE
from xgboost import XGBClassifier

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from gensim.models import Word2Vec, KeyedVectors
from gensim.scripts.glove2word2vec import glove2word2vec

from wordcloud import WordCloud, ImageColorGenerator

import nlpaug.augmenter.char as nac
import nlpaug.augmenter.word as naw
import nlpaug.augmenter.sentence as nas
import nlpaug.flow as naf

from nlpaug.util import Action

from google.cloud import storage
import tensorflow_hub as hub
import tensorflow_text as text

# Ensure warnings are ignored
warnings.filterwarnings("ignore")


df = pd.read_excel('drive/My Drive/AIML/Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx')


df.head()


df = df.replace('\n', '', regex=True)
df['Desc_count'] = df['Description'].apply(lambda x: len(x))


#drop all columns excepts label and text
df.drop([307], inplace = True)
df.drop(['Unnamed: 0', 'Data', 'Countries', 'Local', 'Industry Sector', 'Potential Accident Level', 'Genre', 'Employee or Third Party', 'Critical Risk', 'Desc_count'], axis=1, inplace=True)


X = df.drop(columns=['Accident Level'])
y = df['Accident Level']


df['Accident Level'].value_counts()


#checking the distribition of Accident Label class
fig, ax = plt.subplots(figsize=(20, 10))
sns.countplot(data=df, x='Accident Level', order=['I', 'II','III','IV','V'],ax=ax);


df_ini = df.copy()


# Creating Separate dataframes as per Accident level.
AL_I   = df[df['Accident Level'] == 'I']
AL_II  = df[df['Accident Level'] == 'II']
AL_III = df[df['Accident Level'] == 'III']
AL_IV  = df[df['Accident Level'] == 'IV']
AL_V   = df[df['Accident Level'] == 'V']


from gensim.scripts.glove2word2vec import glove2word2vec
glove_input_file = 'drive/My Drive/AIML/glove.6B.100d.txt'
word2vec_output_file = 'glove.6B.100d.txt.word2vec'
glove2word2vec(glove_input_file, word2vec_output_file)

(400000, 100)


from gensim.models import KeyedVectors
# load the Stanford GloVe model
filename = 'glove.6B.100d.txt.word2vec'
glove_model = KeyedVectors.load_word2vec_format(filename, binary=False)


# Save the Word2Vec model for later use (if necessary)
glove_model.save_word2vec_format('glove.6B.100d.txt.word2vec', binary=True)


import nlpaug.augmenter.word as naw

# Load the correctly formatted Word2Vec model with NLPAug
aug_w2v = naw.WordEmbsAug(model_type='word2vec', model_path='glove.6B.100d.txt.word2vec', action="substitute")


AL_IV_Sample_Df   = AL_IV.sample(n=270, replace=True)

AL_II_Sample_Df  = AL_II.sample(n=260, replace=True)

AL_III_Sample_Df = AL_III.sample(n=272, replace=True)

AL_V_Sample_Df   = AL_V.sample(n=280, replace=True)


# Function to actually augment the description data.
def augmentText(al_df):
    for i, row in al_df.iterrows():
        al_df.at[i, 'Description'] = aug_w2v.augment(row['Description'])


augmentText(AL_IV_Sample_Df)

augmentText(AL_II_Sample_Df)

augmentText(AL_III_Sample_Df)

augmentText(AL_V_Sample_Df)


# Concatenating Augmented dataframes.
frames = [df, AL_IV_Sample_Df, AL_II_Sample_Df, AL_III_Sample_Df, AL_V_Sample_Df]
df_bal = pd.concat(frames)

# Shuffling the records in dataframe as they do remain grouped after concatenation.
df_bal = df_bal.sample(frac = 1).reset_index(drop=True)


df_bal.shape

(1506, 2)


# Save the DataFrame as a CSV file
df_bal.to_csv('drive/My Drive/AIML/df_bal.csv', index=False)

# Alternatively, you can save it as a pickle file for more efficient storage
df_bal.to_pickle('drive/My Drive/AIML/df_bal.pkl')


'''# Load the DataFrame from the CSV file
df_bal_loaded = pd.read_csv('drive/My Drive/AIML/df_bal.csv')
# Load the DataFrame from the pickle file
df_bal_loaded = pd.read_pickle('drive/My Drive/AIML/df_bal.pkl')
'''


df_bal['Accident Level'].value_counts()


fig, ax = plt.subplots(figsize=(20, 10))
sns.countplot(data=df_bal, x='Accident Level',order=['I', 'II','III','IV','V'], ax=ax);


df_bal.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1506 entries, 0 to 1505
Data columns (total 2 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Accident Level  1506 non-null   object
 1   Description     1506 non-null   object
dtypes: object(2)
memory usage: 23.7+ KB


df_bal['Description'][0]

'In circumstances that Mr. Efraín OSORIO FELIX - Mina 2nd. He entered the interior of Pocket 2 at level 2865 (Before -1205). It activated the compressed air gun that is installed in the lower part of the structure with a nozzle that communicates the air lung with the internal part of the pocket, projecting a violent flow of air that blows the left leg of the worker and generates a stun by the noise produced.'


df_nlp=df_bal.copy()


# defining a function to remove special characters
import pandas as pd
import re

def remove_special_characters(text):
    # Defining the regex pattern to match non-alphanumeric characters
    pattern = '[^A-Za-z0-9]+'

    # Finding the specified pattern and replacing non-alphanumeric characters with a blank string
    new_text = ''.join(re.sub(pattern, ' ', text))

    return new_text

def remove_special_characters(text):
    if isinstance(text, str):
        pattern = '[^A-Za-z0-9]+'
        new_text = re.sub(pattern, ' ', text)
        return new_text
    else:
        return text


# Ensure that df_bal has been properly defined
df_bal['cleaned_text'] = df_bal['Description'].apply(remove_special_characters)

# If you need to apply the transformation to df_nlp, make sure df_nlp has the 'Description' column
df_nlp = df_bal.copy()  # Assuming you want to work with df_nlp as a copy of df_bal

# Apply additional transformations to df_nlp
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].str.lower()

# Removing extra whitespaces from the text
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].str.strip()

# Removing extra whitespaces from the text
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].replace(to_replace=r'\d', value='', regex=True)


!pip install nltk
import nltk
from nltk.corpus import stopwords

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import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')
def remove_stopwords(text):
    if isinstance(text, str):
        words = text.split()
        stop_words = set(stopwords.words('english'))
        new_text = ' '.join([word for word in words if word.lower() not in stop_words])
        return new_text
    else:
        return text

[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!


df_nlp['cleaned_text_without_stopwords'] = df_nlp['cleaned_text'].apply(remove_stopwords)


#Tokenize sentence into words
nltk.download('punkt')
df_nlp['cleaned_text_without_stopwords'] = df_nlp['cleaned_text_without_stopwords'].astype(str)
df_nlp['cleaned_text_without_stopwords'] = df_nlp['cleaned_text_without_stopwords'].fillna('')

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!


nltk.data.path.clear()


nltk.data.path.append('/root/nltk_data')


nltk.download('punkt', download_dir='/root/nltk_data')

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!

True


import nltk

# Print the NLTK data paths
print("NLTK data paths:", nltk.data.path)

NLTK data paths: ['/root/nltk_data']


from nltk.tokenize import word_tokenize
tokenizer = nltk.data.load('nltk:tokenizers/punkt/english.pickle')
df_nlp['cleaned_text_stopwords_token'] = df_nlp['cleaned_text_without_stopwords'].apply(lambda x: word_tokenize(x) if isinstance(x, str) else [])


import nltk
nltk.data.path = []  # Clear existing paths
nltk.download('wordnet')
nltk.download('omw-1.4')
nltk.download('punkt')

[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!

True


import spacy
import nltk
from nltk.stem import WordNetLemmatizer

# Initialize NLTK resources
nltk.download('wordnet')
nltk.download('omw-1.4')

# Define NLTK lemmatizer
wnl = WordNetLemmatizer()

# Load SpaCy model
try:
    nlp = spacy.load('en_core_web_sm')
    spacy_available = True
except Exception as e:
    print(f"SpaCy model load error: {e}")
    spacy_available = False

# Define lemmatization function
def apply_lemmatizer(text):
    if isinstance(text, list):
        text = ' '.join(text)  # Convert list back to string

    if spacy_available:
        try:
            doc = nlp(text)
            return ' '.join([token.lemma_ for token in doc])
        except Exception as e:
            print(f"SpaCy lemmatization error: {e}")
            # Fallback to NLTK
            return nltk_lemmatize(text)
    else:
        # Fallback to NLTK if SpaCy is not available
        return nltk_lemmatize(text)

def nltk_lemmatize(text):
    words = text.split()
    return ' '.join([wnl.lemmatize(word) for word in words])

# Apply the function to the DataFrame
df_nlp['final_cleaned_text'] = df_nlp['cleaned_text_stopwords_token'].apply(apply_lemmatizer)

[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!


df_nlp_final_cleaned_text = df_nlp.copy()


# Save the DataFrame as a CSV file
df_nlp_final_cleaned_text.to_csv('drive/My Drive/AIML/df_nlp_final_cleaned_aug_text.csv', index=False)

# Alternatively, you can save it as a pickle file for more efficient storage
df_nlp_final_cleaned_text.to_pickle('drive/My Drive/AIML/df_nlp_final_cleaned_aug_text.pkl')


# Load the DataFrame from a CSV file
df_nlp_aug_text = pd.read_csv('drive/My Drive/AIML/df_nlp_final_cleaned_aug_text.csv')

# Display the DataFrame
print(df_nlp_aug_text.head())

  Accident Level                                        Description  \
0              I  In circumstances that Mr. Efraín OSORIO FELIX ...   
1              I  The employee reports that climbing the access ...   
2              I  During the assembly activity of polypropylene ...   
3              V  In should september not kind 3910, 1986 2007 r...   
4              I  In circumstances in which the operator of bolt...   

                                        cleaned_text  \
0  in circumstances that mr efra n osorio felix m...   
1  the employee reports that climbing the access ...   
2  during the assembly activity of polypropylene ...   
3  in should september not kind    repointed now ...   
4  in circumstances in which the operator of bolt...   

                      cleaned_text_without_stopwords  \
0  circumstances mr efra n osorio felix mina nd e...   
1  employee reports climbing access ladder operat...   
2  assembly activity polypropylene pipe diameter ...   
3  september kind repointed remember antonov puri...   
4  circumstances operator bolter n went ladder he...   

                        cleaned_text_stopwords_token  \
0  ['circumstances', 'mr', 'efra', 'n', 'osorio',...   
1  ['employee', 'reports', 'climbing', 'access', ...   
2  ['assembly', 'activity', 'polypropylene', 'pip...   
3  ['september', 'kind', 'repointed', 'remember',...   
4  ['circumstances', 'operator', 'bolter', 'n', '...   

                                  final_cleaned_text  
0  circumstance mr efra n osorio felix mina nd en...  
1  employee report climb access ladder operating ...  
2  assembly activity polypropylene pipe diameter ...  
3  september kind repointed remember antonov puri...  
4  circumstance operator bolter n go ladder heigh...


df_nlp_aug_text.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1506 entries, 0 to 1505
Data columns (total 6 columns):
 #   Column                          Non-Null Count  Dtype 
---  ------                          --------------  ----- 
 0   Accident Level                  1506 non-null   object
 1   Description                     1506 non-null   object
 2   cleaned_text                    1503 non-null   object
 3   cleaned_text_without_stopwords  1503 non-null   object
 4   cleaned_text_stopwords_token    1506 non-null   object
 5   final_cleaned_text              1503 non-null   object
dtypes: object(6)
memory usage: 70.7+ KB


#remove null values
df_nlp_aug_text.dropna(inplace=True)


df_nlp_aug_text.shape

(1503, 6)


#W2V
# creating a list of all words in our data
words_list = [item.split(" ") for item in df_nlp_aug_text['final_cleaned_text'].values]


from tensorflow.keras.preprocessing.text import Tokenizer

# Assuming words_list is your list of sentences (a list of lists of words)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(words_list)

# Get the word index from the tokenizer
word_index = tokenizer.word_index

sequences = tokenizer.texts_to_sequences(words_list)

# Calculate sentence lengths
sentence_lengths = [len(seq) for seq in sequences]
max_sentence_length = max(sentence_lengths)


from tensorflow.keras.preprocessing.sequence import pad_sequences

# Pad sequences to ensure uniform length
X_padded = pad_sequences(sequences, maxlen=max_sentence_length)

print("Padded sequences shape:", X_padded.shape)

Padded sequences shape: (1503, 110)


# creating an instance of Word2Vec
model_W2V = Word2Vec(words_list, vector_size = 100, min_count = 1, window=5, workers = 6)
# Checking the size of the vocabulary
print("Length of the vocabulary is", len(list(model_W2V.wv.key_to_index)))

Length of the vocabulary is 3657


# Dictionary with key as words and the value as the embedding vector.

words = list(model_W2V.wv.key_to_index.keys())

# Retrieving word vectors for all the words present in the model's vocabulary
wvs = model_W2V.wv[words].tolist()

# Creating a dictionary of words and their corresponding vectors
word_vector_dict = dict(zip(words, wvs))


import numpy as np

# Define the size of the embedding
embedding_dim = 100  # This should match the vector_size used in Word2Vec
vocab_size = len(word_index) + 1  # +1 because Keras word indices are 1-based

# Initialize embedding matrix with zeros
embedding_matrix = np.zeros((vocab_size, embedding_dim))

# Fill the embedding matrix with Word2Vec vectors
for word, i in word_index.items():
    if word in word_vector_dict:  # Check if the word exists in the Word2Vec model
        embedding_matrix[i] = word_vector_dict[word]


embedding_matrix

array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,
         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
       [-5.77343106e-01,  4.82772678e-01,  1.91145718e-01, ...,
        -7.84745216e-01,  3.77532899e-01, -1.92808583e-01],
       [-7.25353718e-01,  6.23810112e-01,  1.13283016e-01, ...,
        -8.53813112e-01,  3.41728956e-01, -5.54654235e-03],
       ...,
       [-3.18922065e-02,  9.77649260e-03, -1.90693582e-03, ...,
        -2.78429203e-02,  4.53467155e-03,  1.72639760e-04],
       [-1.63868573e-02,  2.06665788e-02,  4.56927482e-05, ...,
        -3.29611674e-02,  6.21473650e-03, -4.04176972e-05],
       [-1.52574629e-02,  1.88009255e-02,  1.26959132e-02, ...,
        -2.31007691e-02,  9.62041225e-03,  3.88677116e-04]])


print (vocab_size)
print (embedding_matrix.shape)
print (max_sentence_length)
print (embedding_dim)

3658
(3658, 100)
110
100


y = df_nlp_aug_text['Accident Level']


!pip install scikit-learn  # Install scikit-learn if you haven't already
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse_output=False)  # Create OneHotEncoder instance
y_encoded = encoder.fit_transform(y.values.reshape(-1, 1))  # Reshape and encode

Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.5.1)
Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)
Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)
Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)
Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)


y_encoded

array([[1., 0., 0., 0., 0.],
       [1., 0., 0., 0., 0.],
       [1., 0., 0., 0., 0.],
       ...,
       [0., 0., 0., 0., 1.],
       [0., 0., 0., 1., 0.],
       [0., 0., 0., 1., 0.]])


X_train, X_test, y_train, y_test = train_test_split(X_padded, y_encoded, test_size=0.2,stratify=y, random_state=42)


print(f"X_train shape: {X_train.shape}, y_train shape: {y_train.shape}")
print(f"X_test shape: {X_test.shape}, y_test shape: {y_test.shape}")

X_train shape: (1202, 110), y_train shape: (1202, 5)
X_test shape: (301, 110), y_test shape: (301, 5)


def plotLossChart(history):
    plt.figure(figsize=(16,5))
    epochs = range(1, len(history.history['accuracy'])+1)
    plt.plot(epochs, history.history['loss'], 'b', label='Training Loss', color='red')
    plt.plot(epochs, history.history['val_loss'], 'b', label='Validation Loss')
    plt.legend()
    plt.show()


def plotAccuracyChart(history):
    plt.figure(figsize=(16,5))
    epochs = range(1, len(history.history['accuracy'])+1)
    plt.plot(epochs, history.history['accuracy'], 'b', label='Training Accuracy', color='red')
    plt.plot(epochs, history.history['val_accuracy'], 'b', label='Validation Accuracy')
    plt.legend()
    plt.show()


def plotAccuracyChart(history):
    plt.figure(figsize=(16,5))
    epochs = range(1, len(history.history['accuracy'])+1)
    plt.plot(epochs, history.history['accuracy'], 'b', label='Training Accuracy', color='red')
    plt.plot(epochs, history.history['val_accuracy'], 'b', label='Validation Accuracy')
    plt.legend()
    plt.show()


import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report

def evaluate_model_performance(model, X_test, y_test, class_labels=['I', 'II', 'III', 'IV', 'V']):
    """
    Evaluates the model performance by generating a confusion matrix and classification report.

    Parameters:
    - model: Trained Keras model
    - X_test: Test features
    - y_test: True labels for the test data
    - class_labels: List of class labels for the confusion matrix and classification report

    Returns:
    - None
    """
    # Predict on the test set
    y_pred = model.predict(X_test)

    # Convert predictions and true labels from one-hot encoding to discrete labels
    y_pred_discrete = np.argmax(y_pred, axis=1)
    y_test_discrete = np.argmax(y_test, axis=1)

    # Compute confusion matrix
    cm = confusion_matrix(y_test_discrete, y_pred_discrete)

    # Plot confusion matrix
    plt.figure(figsize=(10, 7))
    sns.heatmap(cm,
                annot=True,
                fmt='g',
                xticklabels=class_labels,
                yticklabels=class_labels)
    plt.ylabel('Actual', fontsize=13)
    plt.title('Confusion Matrix', fontsize=17, pad=20)
    plt.gca().xaxis.set_label_position('top')
    plt.xlabel('Prediction', fontsize=13)
    plt.gca().xaxis.tick_top()
    plt.gca().figure.subplots_adjust(bottom=0.2)
    plt.gca().figure.text(0.5, 0.05, 'Prediction', ha='center', fontsize=13)
    plt.show()

    # Print classification report
    report = classification_report(y_test_discrete, y_pred_discrete, target_names=class_labels)
    print(report)


from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger

# Define the model
model = Sequential()

# Create the embedding layer
embedding_layer = Embedding(input_dim=vocab_size,
                            output_dim=embedding_dim,
                            weights=[embedding_matrix],
                            input_length=max_sentence_length,  # sentence_length is the length of input sequences
                            trainable=False)  # Set trainable to False to use the embeddings as they are
model.add(embedding_layer)

# Add other layers (LSTM, Dense, etc.)
model.add(LSTM(128, dropout=0.3, return_sequences=True))
model.add(LSTM(64, dropout=0.3))
model.add(Dense(5, activation='softmax'))

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Model callbacks
callbacks = [
    ModelCheckpoint('best_model_lstm.keras', save_best_only=True, monitor='val_accuracy', mode='max', verbose=1),
    CSVLogger('training_log.csv')
]


history_lstm=model.fit(X_train, y_train, epochs=80, batch_size=32, validation_data=(X_test, y_test),callbacks=callbacks)

Epoch 1/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - accuracy: 0.1899 - loss: 1.6174
Epoch 1: val_accuracy improved from -inf to 0.36545, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 7s 28ms/step - accuracy: 0.1928 - loss: 1.6143 - val_accuracy: 0.3654 - val_loss: 1.4214
Epoch 2/80
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.3693 - loss: 1.3252
Epoch 2: val_accuracy improved from 0.36545 to 0.43854, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.3714 - loss: 1.3225 - val_accuracy: 0.4385 - val_loss: 1.2405
Epoch 3/80
33/38 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.4903 - loss: 1.1688
Epoch 3: val_accuracy improved from 0.43854 to 0.47508, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.4885 - loss: 1.1674 - val_accuracy: 0.4751 - val_loss: 1.1553
Epoch 4/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.5273 - loss: 1.1092
Epoch 4: val_accuracy improved from 0.47508 to 0.49169, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.5258 - loss: 1.1110 - val_accuracy: 0.4917 - val_loss: 1.0762
Epoch 5/80
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.5016 - loss: 1.1550
Epoch 5: val_accuracy improved from 0.49169 to 0.49834, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5013 - loss: 1.1552 - val_accuracy: 0.4983 - val_loss: 1.1316
Epoch 6/80
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.5054 - loss: 1.1230
Epoch 6: val_accuracy improved from 0.49834 to 0.52159, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.5054 - loss: 1.1225 - val_accuracy: 0.5216 - val_loss: 1.0728
Epoch 7/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.5230 - loss: 1.0566
Epoch 7: val_accuracy did not improve from 0.52159
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step - accuracy: 0.5225 - loss: 1.0602 - val_accuracy: 0.5083 - val_loss: 1.0925
Epoch 8/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.5733 - loss: 1.0233
Epoch 8: val_accuracy improved from 0.52159 to 0.54485, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.5704 - loss: 1.0248 - val_accuracy: 0.5449 - val_loss: 1.0244
Epoch 9/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.5905 - loss: 0.9662
Epoch 9: val_accuracy did not improve from 0.54485
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.5893 - loss: 0.9671 - val_accuracy: 0.5282 - val_loss: 1.0672
Epoch 10/80
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.5786 - loss: 0.9760
Epoch 10: val_accuracy did not improve from 0.54485
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.5782 - loss: 0.9762 - val_accuracy: 0.5216 - val_loss: 1.0065
Epoch 11/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.5810 - loss: 0.9263
Epoch 11: val_accuracy did not improve from 0.54485
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.5800 - loss: 0.9276 - val_accuracy: 0.5349 - val_loss: 1.1112
Epoch 12/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6106 - loss: 0.9196
Epoch 12: val_accuracy did not improve from 0.54485
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.6079 - loss: 0.9222 - val_accuracy: 0.5316 - val_loss: 0.9825
Epoch 13/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6104 - loss: 0.9303
Epoch 13: val_accuracy improved from 0.54485 to 0.57807, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.6085 - loss: 0.9315 - val_accuracy: 0.5781 - val_loss: 0.9912
Epoch 14/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.5830 - loss: 0.9311
Epoch 14: val_accuracy did not improve from 0.57807
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.5831 - loss: 0.9317 - val_accuracy: 0.5648 - val_loss: 0.9721
Epoch 15/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.5949 - loss: 0.9116
Epoch 15: val_accuracy did not improve from 0.57807
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.5957 - loss: 0.9099 - val_accuracy: 0.5681 - val_loss: 0.9565
Epoch 16/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6012 - loss: 0.9134
Epoch 16: val_accuracy did not improve from 0.57807
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6009 - loss: 0.9147 - val_accuracy: 0.5781 - val_loss: 0.9977
Epoch 17/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6079 - loss: 0.9059
Epoch 17: val_accuracy did not improve from 0.57807
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.6071 - loss: 0.9070 - val_accuracy: 0.5382 - val_loss: 0.9651
Epoch 18/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6420 - loss: 0.8174
Epoch 18: val_accuracy did not improve from 0.57807
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.6411 - loss: 0.8210 - val_accuracy: 0.5249 - val_loss: 1.0040
Epoch 19/80
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.6339 - loss: 0.8696
Epoch 19: val_accuracy did not improve from 0.57807
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.6338 - loss: 0.8696 - val_accuracy: 0.5748 - val_loss: 0.9686
Epoch 20/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6030 - loss: 0.8983
Epoch 20: val_accuracy improved from 0.57807 to 0.60465, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.6061 - loss: 0.8944 - val_accuracy: 0.6047 - val_loss: 0.9154
Epoch 21/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6590 - loss: 0.8139
Epoch 21: val_accuracy did not improve from 0.60465
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.6573 - loss: 0.8159 - val_accuracy: 0.6047 - val_loss: 0.9083
Epoch 22/80
34/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6585 - loss: 0.7768
Epoch 22: val_accuracy improved from 0.60465 to 0.61794, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.6561 - loss: 0.7807 - val_accuracy: 0.6179 - val_loss: 0.8966
Epoch 23/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.6820 - loss: 0.7950
Epoch 23: val_accuracy improved from 0.61794 to 0.63123, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.6809 - loss: 0.7950 - val_accuracy: 0.6312 - val_loss: 0.8950
Epoch 24/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.6538 - loss: 0.7609
Epoch 24: val_accuracy improved from 0.63123 to 0.65781, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step - accuracy: 0.6551 - loss: 0.7603 - val_accuracy: 0.6578 - val_loss: 0.8051
Epoch 25/80
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.6913 - loss: 0.7524
Epoch 25: val_accuracy improved from 0.65781 to 0.67442, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.6912 - loss: 0.7524 - val_accuracy: 0.6744 - val_loss: 0.8315
Epoch 26/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.6528 - loss: 0.7921
Epoch 26: val_accuracy did not improve from 0.67442
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - accuracy: 0.6559 - loss: 0.7874 - val_accuracy: 0.6213 - val_loss: 0.8769
Epoch 27/80
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.7279 - loss: 0.6650
Epoch 27: val_accuracy improved from 0.67442 to 0.68771, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step - accuracy: 0.7276 - loss: 0.6654 - val_accuracy: 0.6877 - val_loss: 0.8057
Epoch 28/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.6926 - loss: 0.7364
Epoch 28: val_accuracy improved from 0.68771 to 0.69435, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step - accuracy: 0.6956 - loss: 0.7328 - val_accuracy: 0.6944 - val_loss: 0.7419
Epoch 29/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7529 - loss: 0.6399
Epoch 29: val_accuracy improved from 0.69435 to 0.70432, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.7511 - loss: 0.6423 - val_accuracy: 0.7043 - val_loss: 0.7649
Epoch 30/80
34/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7766 - loss: 0.6196
Epoch 30: val_accuracy did not improve from 0.70432
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.7756 - loss: 0.6186 - val_accuracy: 0.7043 - val_loss: 0.7227
Epoch 31/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.7728 - loss: 0.5764
Epoch 31: val_accuracy improved from 0.70432 to 0.75083, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.7727 - loss: 0.5772 - val_accuracy: 0.7508 - val_loss: 0.7012
Epoch 32/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8129 - loss: 0.5498
Epoch 32: val_accuracy improved from 0.75083 to 0.79734, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8120 - loss: 0.5511 - val_accuracy: 0.7973 - val_loss: 0.5900
Epoch 33/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8075 - loss: 0.5524
Epoch 33: val_accuracy did not improve from 0.79734
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8079 - loss: 0.5514 - val_accuracy: 0.7641 - val_loss: 0.6401
Epoch 34/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8310 - loss: 0.5045
Epoch 34: val_accuracy did not improve from 0.79734
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8302 - loss: 0.5049 - val_accuracy: 0.7741 - val_loss: 0.6344
Epoch 35/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8477 - loss: 0.4682
Epoch 35: val_accuracy did not improve from 0.79734
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8461 - loss: 0.4704 - val_accuracy: 0.7741 - val_loss: 0.6246
Epoch 36/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8350 - loss: 0.4361
Epoch 36: val_accuracy did not improve from 0.79734
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8347 - loss: 0.4368 - val_accuracy: 0.7940 - val_loss: 0.5399
Epoch 37/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8534 - loss: 0.4250
Epoch 37: val_accuracy improved from 0.79734 to 0.85050, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8529 - loss: 0.4261 - val_accuracy: 0.8505 - val_loss: 0.4714
Epoch 38/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.8735 - loss: 0.3687
Epoch 38: val_accuracy did not improve from 0.85050
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8736 - loss: 0.3706 - val_accuracy: 0.7973 - val_loss: 0.6279
Epoch 39/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8653 - loss: 0.3991
Epoch 39: val_accuracy improved from 0.85050 to 0.85382, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8650 - loss: 0.3997 - val_accuracy: 0.8538 - val_loss: 0.4864
Epoch 40/80
34/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8807 - loss: 0.3552
Epoch 40: val_accuracy did not improve from 0.85382
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.8804 - loss: 0.3560 - val_accuracy: 0.8538 - val_loss: 0.4446
Epoch 41/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8933 - loss: 0.3205
Epoch 41: val_accuracy improved from 0.85382 to 0.86047, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8934 - loss: 0.3217 - val_accuracy: 0.8605 - val_loss: 0.4434
Epoch 42/80
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8724 - loss: 0.3647
Epoch 42: val_accuracy improved from 0.86047 to 0.87708, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - accuracy: 0.8725 - loss: 0.3643 - val_accuracy: 0.8771 - val_loss: 0.3784
Epoch 43/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9017 - loss: 0.3197
Epoch 43: val_accuracy did not improve from 0.87708
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9007 - loss: 0.3230 - val_accuracy: 0.8571 - val_loss: 0.4448
Epoch 44/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.8821 - loss: 0.3401
Epoch 44: val_accuracy improved from 0.87708 to 0.88372, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.8833 - loss: 0.3385 - val_accuracy: 0.8837 - val_loss: 0.3626
Epoch 45/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.8920 - loss: 0.3084
Epoch 45: val_accuracy did not improve from 0.88372
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - accuracy: 0.8933 - loss: 0.3052 - val_accuracy: 0.8837 - val_loss: 0.4078
Epoch 46/80
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - accuracy: 0.9166 - loss: 0.2648
Epoch 46: val_accuracy did not improve from 0.88372
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step - accuracy: 0.9165 - loss: 0.2648 - val_accuracy: 0.8173 - val_loss: 0.5427
Epoch 47/80
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.8893 - loss: 0.3224
Epoch 47: val_accuracy did not improve from 0.88372
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - accuracy: 0.8898 - loss: 0.3209 - val_accuracy: 0.8771 - val_loss: 0.3628
Epoch 48/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9174 - loss: 0.2550
Epoch 48: val_accuracy did not improve from 0.88372
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9167 - loss: 0.2572 - val_accuracy: 0.8837 - val_loss: 0.4173
Epoch 49/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9143 - loss: 0.2596
Epoch 49: val_accuracy improved from 0.88372 to 0.89369, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9147 - loss: 0.2582 - val_accuracy: 0.8937 - val_loss: 0.3650
Epoch 50/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9347 - loss: 0.2094
Epoch 50: val_accuracy did not improve from 0.89369
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9329 - loss: 0.2137 - val_accuracy: 0.8804 - val_loss: 0.3607
Epoch 51/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9202 - loss: 0.2327
Epoch 51: val_accuracy did not improve from 0.89369
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9195 - loss: 0.2341 - val_accuracy: 0.8771 - val_loss: 0.3970
Epoch 52/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9364 - loss: 0.2125
Epoch 52: val_accuracy did not improve from 0.89369
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9360 - loss: 0.2130 - val_accuracy: 0.8904 - val_loss: 0.3422
Epoch 53/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9095 - loss: 0.2722
Epoch 53: val_accuracy improved from 0.89369 to 0.90033, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - accuracy: 0.9092 - loss: 0.2725 - val_accuracy: 0.9003 - val_loss: 0.3391
Epoch 54/80
34/38 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9125 - loss: 0.2545
Epoch 54: val_accuracy did not improve from 0.90033
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9129 - loss: 0.2538 - val_accuracy: 0.8505 - val_loss: 0.4004
Epoch 55/80
34/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9139 - loss: 0.2506
Epoch 55: val_accuracy improved from 0.90033 to 0.90365, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9153 - loss: 0.2481 - val_accuracy: 0.9037 - val_loss: 0.3279
Epoch 56/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9293 - loss: 0.2180
Epoch 56: val_accuracy did not improve from 0.90365
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9295 - loss: 0.2166 - val_accuracy: 0.8837 - val_loss: 0.3926
Epoch 57/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9480 - loss: 0.1798
Epoch 57: val_accuracy did not improve from 0.90365
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9468 - loss: 0.1818 - val_accuracy: 0.9037 - val_loss: 0.3589
Epoch 58/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9178 - loss: 0.2265
Epoch 58: val_accuracy improved from 0.90365 to 0.91030, saving model to best_model_lstm.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step - accuracy: 0.9187 - loss: 0.2251 - val_accuracy: 0.9103 - val_loss: 0.3080
Epoch 59/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9328 - loss: 0.1765
Epoch 59: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.9331 - loss: 0.1768 - val_accuracy: 0.8937 - val_loss: 0.4126
Epoch 60/80
34/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9307 - loss: 0.2050
Epoch 60: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9324 - loss: 0.2020 - val_accuracy: 0.9037 - val_loss: 0.3059
Epoch 61/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9449 - loss: 0.1724
Epoch 61: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step - accuracy: 0.9447 - loss: 0.1726 - val_accuracy: 0.8837 - val_loss: 0.3578
Epoch 62/80
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9398 - loss: 0.1762
Epoch 62: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9397 - loss: 0.1770 - val_accuracy: 0.8904 - val_loss: 0.3889
Epoch 63/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9402 - loss: 0.1772
Epoch 63: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9402 - loss: 0.1770 - val_accuracy: 0.8904 - val_loss: 0.3394
Epoch 64/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9231 - loss: 0.2177
Epoch 64: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9246 - loss: 0.2148 - val_accuracy: 0.8904 - val_loss: 0.3599
Epoch 65/80
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9489 - loss: 0.1549
Epoch 65: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - accuracy: 0.9485 - loss: 0.1562 - val_accuracy: 0.8937 - val_loss: 0.3586
Epoch 66/80
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9543 - loss: 0.1428
Epoch 66: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - accuracy: 0.9539 - loss: 0.1435 - val_accuracy: 0.8804 - val_loss: 0.3818
Epoch 67/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step - accuracy: 0.9426 - loss: 0.1478
Epoch 67: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step - accuracy: 0.9434 - loss: 0.1471 - val_accuracy: 0.8804 - val_loss: 0.3777
Epoch 68/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - accuracy: 0.9378 - loss: 0.1777
Epoch 68: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - accuracy: 0.9381 - loss: 0.1769 - val_accuracy: 0.8904 - val_loss: 0.3721
Epoch 69/80
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9534 - loss: 0.1428
Epoch 69: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9531 - loss: 0.1431 - val_accuracy: 0.8937 - val_loss: 0.3477
Epoch 70/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9434 - loss: 0.1616
Epoch 70: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9438 - loss: 0.1611 - val_accuracy: 0.8904 - val_loss: 0.3889
Epoch 71/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9454 - loss: 0.1482
Epoch 71: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step - accuracy: 0.9456 - loss: 0.1485 - val_accuracy: 0.8904 - val_loss: 0.3555
Epoch 72/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9432 - loss: 0.1791
Epoch 72: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - accuracy: 0.9434 - loss: 0.1783 - val_accuracy: 0.8937 - val_loss: 0.3522
Epoch 73/80
34/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9524 - loss: 0.1596
Epoch 73: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9524 - loss: 0.1592 - val_accuracy: 0.8738 - val_loss: 0.4100
Epoch 74/80
34/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9482 - loss: 0.1540
Epoch 74: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9481 - loss: 0.1541 - val_accuracy: 0.8904 - val_loss: 0.3520
Epoch 75/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9499 - loss: 0.1395
Epoch 75: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9498 - loss: 0.1398 - val_accuracy: 0.8870 - val_loss: 0.3673
Epoch 76/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9441 - loss: 0.1650
Epoch 76: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9428 - loss: 0.1670 - val_accuracy: 0.8970 - val_loss: 0.3379
Epoch 77/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9380 - loss: 0.1651
Epoch 77: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9386 - loss: 0.1646 - val_accuracy: 0.9003 - val_loss: 0.3469
Epoch 78/80
34/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9410 - loss: 0.1620
Epoch 78: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9415 - loss: 0.1598 - val_accuracy: 0.9003 - val_loss: 0.3441
Epoch 79/80
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9502 - loss: 0.1338
Epoch 79: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9513 - loss: 0.1324 - val_accuracy: 0.8970 - val_loss: 0.3586
Epoch 80/80
35/38 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - accuracy: 0.9478 - loss: 0.1264
Epoch 80: val_accuracy did not improve from 0.91030
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step - accuracy: 0.9491 - loss: 0.1251 - val_accuracy: 0.8937 - val_loss: 0.3981


# Display model summary
model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ embedding (Embedding)                │ (None, 110, 100)            │         365,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm (LSTM)                          │ (None, 110, 128)            │         117,248 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_1 (LSTM)                        │ (None, 64)                  │          49,408 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense (Dense)                        │ (None, 5)                   │             325 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 866,745 (3.31 MB)

 Trainable params: 166,981 (652.27 KB)

 Non-trainable params: 365,800 (1.40 MB)

 Optimizer params: 333,964 (1.27 MB)


from tensorflow.keras.utils import plot_model

# Assuming you have a defined model named 'model'
plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True, dpi=100)


from tensorflow.keras.models import load_model

# Load the model with the best weights
model = load_model('best_model_lstm.keras')


train_loss, train_accuracy = model.evaluate(X_train, y_train)
print(f"Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}")

test_loss, test_accuracy = model.evaluate(X_test, y_test)
print(f"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}")

38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step - accuracy: 0.9468 - loss: 0.1607
Train Loss: 0.1714, Train Accuracy: 0.9409
10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.9237 - loss: 0.2983
Test Loss: 0.3007, Test Accuracy: 0.9169


evaluate_model_performance(model, X_test, y_test)

10/10 ━━━━━━━━━━━━━━━━━━━━ 1s 33ms/step

              precision    recall  f1-score   support

           I       0.78      0.89      0.83        63
          II       0.88      0.83      0.85        60
         III       0.98      0.93      0.96        61
          IV       0.92      0.92      0.92        60
           V       1.00      0.95      0.97        57

    accuracy                           0.90       301
   macro avg       0.91      0.90      0.91       301
weighted avg       0.91      0.90      0.91       301


y_pred = model.predict(X_test)
F1_score= f1_score(np.argmax(y_test, axis=1), np.argmax(y_pred, axis=1), average='weighted')
print(f"F1 Score: {F1_score:.4f}")
tempResult = pd.DataFrame({'Data Description':"LSTM on W2V using augmented data",
                                'Model Description':"LSTM on W2V-augmented dataset ",
                                'Train Accuracy':round(train_accuracy*100,2),
                                'Test Accuracy':round(test_accuracy*100,2),
                                'F1 Score':round(F1_score*100,2),
                                }, index=[0])

10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step
F1 Score: 0.9020


#combinedNNModelResult = pd.concat([combinedNNModelResult, temp_result])
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{tempResult}\n')

Collating Results : 
-----------------------
                   Data Description               Model Description  Train Accuracy  Test Accuracy  F1 Score
0  LSTM on W2V using augmented data  LSTM on W2V-augmented dataset            94.09          91.69      90.2


from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional
from tensorflow.keras.callbacks import ModelCheckpoint

# Define the model
model_Bi_LSTM = Sequential()

# Create the embedding layer
embedding_layer = Embedding(input_dim=vocab_size,
                            output_dim=embedding_dim,
                            weights=[embedding_matrix],
                            input_length=max_sentence_length,  # sentence_length is the length of input sequences
                            trainable=False)  # Set trainable to False to use the embeddings as they are
model_Bi_LSTM.add(embedding_layer)

# Add a Bidirectional LSTM layer with multiple layers
model_Bi_LSTM.add(Bidirectional(LSTM(128, return_sequences=True)))  # Return sequences for next LSTM layer
model_Bi_LSTM.add(Dropout(0.4))  # Adjust dropout rate

model_Bi_LSTM.add(Bidirectional(LSTM(64)))  # Add another LSTM layer
model_Bi_LSTM.add(Dropout(0.4))  # Adjust dropout rate

# Add a Dense layer
model_Bi_LSTM.add(Dense(5, activation='softmax'))

# Compile the model
model_Bi_LSTM.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Model callbacks
callbacks = [
    ModelCheckpoint('best_BiLSTM_model.keras', save_best_only=True, monitor='val_accuracy', mode='max', verbose=1)
]


# Train the model
history_bi_lstm = model_Bi_LSTM.fit(X_train, y_train,
                         epochs=100,
                         batch_size=32,
                         validation_data=(X_test, y_test),
                         callbacks=callbacks)

Epoch 1/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 23ms/step - accuracy: 0.2697 - loss: 1.5613
Epoch 1: val_accuracy improved from -inf to 0.35548, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 6s 40ms/step - accuracy: 0.2717 - loss: 1.5583 - val_accuracy: 0.3555 - val_loss: 1.4835
Epoch 2/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.4687 - loss: 1.1739
Epoch 2: val_accuracy improved from 0.35548 to 0.54485, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.4710 - loss: 1.1699 - val_accuracy: 0.5449 - val_loss: 1.0559
Epoch 3/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.5424 - loss: 1.0027
Epoch 3: val_accuracy improved from 0.54485 to 0.58472, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.5433 - loss: 1.0018 - val_accuracy: 0.5847 - val_loss: 0.9334
Epoch 4/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.5135 - loss: 1.0775
Epoch 4: val_accuracy did not improve from 0.58472
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.5107 - loss: 1.0848 - val_accuracy: 0.4319 - val_loss: 1.2136
Epoch 5/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.5300 - loss: 1.0969
Epoch 5: val_accuracy did not improve from 0.58472
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.5308 - loss: 1.0948 - val_accuracy: 0.5847 - val_loss: 0.9682
Epoch 6/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.5613 - loss: 1.0147
Epoch 6: val_accuracy improved from 0.58472 to 0.61794, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.5605 - loss: 1.0155 - val_accuracy: 0.6179 - val_loss: 0.9397
Epoch 7/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.5914 - loss: 0.9179
Epoch 7: val_accuracy improved from 0.61794 to 0.63455, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.5910 - loss: 0.9185 - val_accuracy: 0.6346 - val_loss: 0.8883
Epoch 8/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.5993 - loss: 0.9307
Epoch 8: val_accuracy did not improve from 0.63455
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - accuracy: 0.5995 - loss: 0.9300 - val_accuracy: 0.6179 - val_loss: 0.8986
Epoch 9/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step - accuracy: 0.5783 - loss: 0.9317
Epoch 9: val_accuracy did not improve from 0.63455
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 31ms/step - accuracy: 0.5788 - loss: 0.9325 - val_accuracy: 0.4950 - val_loss: 1.0739
Epoch 10/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.5536 - loss: 1.0216
Epoch 10: val_accuracy did not improve from 0.63455
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 33ms/step - accuracy: 0.5542 - loss: 1.0203 - val_accuracy: 0.5980 - val_loss: 0.9371
Epoch 11/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6414 - loss: 0.8660
Epoch 11: val_accuracy did not improve from 0.63455
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - accuracy: 0.6398 - loss: 0.8673 - val_accuracy: 0.5914 - val_loss: 0.9004
Epoch 12/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6229 - loss: 0.8743
Epoch 12: val_accuracy did not improve from 0.63455
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6240 - loss: 0.8728 - val_accuracy: 0.6312 - val_loss: 0.8563
Epoch 13/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6422 - loss: 0.8227
Epoch 13: val_accuracy did not improve from 0.63455
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.6416 - loss: 0.8255 - val_accuracy: 0.6213 - val_loss: 0.9299
Epoch 14/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6282 - loss: 0.8434
Epoch 14: val_accuracy improved from 0.63455 to 0.63787, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.6283 - loss: 0.8438 - val_accuracy: 0.6379 - val_loss: 0.8825
Epoch 15/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6432 - loss: 0.8462
Epoch 15: val_accuracy did not improve from 0.63787
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6430 - loss: 0.8459 - val_accuracy: 0.6213 - val_loss: 0.8458
Epoch 16/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6623 - loss: 0.8020
Epoch 16: val_accuracy did not improve from 0.63787
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.6617 - loss: 0.8034 - val_accuracy: 0.6312 - val_loss: 0.8387
Epoch 17/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.6869 - loss: 0.7657
Epoch 17: val_accuracy did not improve from 0.63787
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.6861 - loss: 0.7676 - val_accuracy: 0.6146 - val_loss: 0.8449
Epoch 18/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6722 - loss: 0.7783
Epoch 18: val_accuracy improved from 0.63787 to 0.65116, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step - accuracy: 0.6726 - loss: 0.7786 - val_accuracy: 0.6512 - val_loss: 0.8503
Epoch 19/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.6692 - loss: 0.8033
Epoch 19: val_accuracy improved from 0.65116 to 0.73090, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 35ms/step - accuracy: 0.6699 - loss: 0.8023 - val_accuracy: 0.7309 - val_loss: 0.7730
Epoch 20/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 22ms/step - accuracy: 0.7046 - loss: 0.7522
Epoch 20: val_accuracy did not improve from 0.73090
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 25ms/step - accuracy: 0.7044 - loss: 0.7525 - val_accuracy: 0.7010 - val_loss: 0.7887
Epoch 21/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.7020 - loss: 0.7339
Epoch 21: val_accuracy did not improve from 0.73090
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.7017 - loss: 0.7352 - val_accuracy: 0.6977 - val_loss: 0.8473
Epoch 22/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.6968 - loss: 0.7591
Epoch 22: val_accuracy did not improve from 0.73090
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.6968 - loss: 0.7593 - val_accuracy: 0.6811 - val_loss: 0.7755
Epoch 23/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.7082 - loss: 0.7372
Epoch 23: val_accuracy did not improve from 0.73090
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.7072 - loss: 0.7389 - val_accuracy: 0.6910 - val_loss: 0.7617
Epoch 24/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.7232 - loss: 0.7074
Epoch 24: val_accuracy did not improve from 0.73090
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step - accuracy: 0.7237 - loss: 0.7062 - val_accuracy: 0.6977 - val_loss: 0.7746
Epoch 25/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.7093 - loss: 0.7437
Epoch 25: val_accuracy did not improve from 0.73090
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.7103 - loss: 0.7420 - val_accuracy: 0.7243 - val_loss: 0.7365
Epoch 26/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.7563 - loss: 0.6458
Epoch 26: val_accuracy did not improve from 0.73090
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.7563 - loss: 0.6459 - val_accuracy: 0.7309 - val_loss: 0.6969
Epoch 27/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.7563 - loss: 0.6491
Epoch 27: val_accuracy improved from 0.73090 to 0.78738, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.7571 - loss: 0.6478 - val_accuracy: 0.7874 - val_loss: 0.6746
Epoch 28/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.7981 - loss: 0.6181
Epoch 28: val_accuracy did not improve from 0.78738
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.7972 - loss: 0.6184 - val_accuracy: 0.7010 - val_loss: 0.7263
Epoch 29/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step - accuracy: 0.7563 - loss: 0.6271
Epoch 29: val_accuracy did not improve from 0.78738
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step - accuracy: 0.7567 - loss: 0.6269 - val_accuracy: 0.7508 - val_loss: 0.6683
Epoch 30/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.7964 - loss: 0.5675
Epoch 30: val_accuracy did not improve from 0.78738
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 33ms/step - accuracy: 0.7980 - loss: 0.5655 - val_accuracy: 0.7774 - val_loss: 0.6490
Epoch 31/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step - accuracy: 0.7819 - loss: 0.5882
Epoch 31: val_accuracy did not improve from 0.78738
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 30ms/step - accuracy: 0.7821 - loss: 0.5885 - val_accuracy: 0.7741 - val_loss: 0.6574
Epoch 32/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8003 - loss: 0.5963
Epoch 32: val_accuracy improved from 0.78738 to 0.83721, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step - accuracy: 0.8007 - loss: 0.5946 - val_accuracy: 0.8372 - val_loss: 0.5394
Epoch 33/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8518 - loss: 0.4357
Epoch 33: val_accuracy did not improve from 0.83721
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.8503 - loss: 0.4396 - val_accuracy: 0.7973 - val_loss: 0.5996
Epoch 34/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.7792 - loss: 0.5819
Epoch 34: val_accuracy improved from 0.83721 to 0.86047, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step - accuracy: 0.7800 - loss: 0.5805 - val_accuracy: 0.8605 - val_loss: 0.4861
Epoch 35/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8759 - loss: 0.4121
Epoch 35: val_accuracy improved from 0.86047 to 0.86379, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.8756 - loss: 0.4121 - val_accuracy: 0.8638 - val_loss: 0.4675
Epoch 36/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8730 - loss: 0.3883
Epoch 36: val_accuracy did not improve from 0.86379
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.8718 - loss: 0.3909 - val_accuracy: 0.7076 - val_loss: 0.7768
Epoch 37/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8250 - loss: 0.5018
Epoch 37: val_accuracy did not improve from 0.86379
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step - accuracy: 0.8262 - loss: 0.4991 - val_accuracy: 0.8505 - val_loss: 0.4418
Epoch 38/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.8761 - loss: 0.3556
Epoch 38: val_accuracy improved from 0.86379 to 0.87375, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.8760 - loss: 0.3562 - val_accuracy: 0.8738 - val_loss: 0.4330
Epoch 39/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 19ms/step - accuracy: 0.8818 - loss: 0.3748
Epoch 39: val_accuracy did not improve from 0.87375
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.8810 - loss: 0.3762 - val_accuracy: 0.8638 - val_loss: 0.4636
Epoch 40/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8819 - loss: 0.3983
Epoch 40: val_accuracy did not improve from 0.87375
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.8821 - loss: 0.3965 - val_accuracy: 0.8704 - val_loss: 0.4318
Epoch 41/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.9002 - loss: 0.3625
Epoch 41: val_accuracy did not improve from 0.87375
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step - accuracy: 0.8996 - loss: 0.3613 - val_accuracy: 0.8073 - val_loss: 0.5720
Epoch 42/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.8650 - loss: 0.4386
Epoch 42: val_accuracy improved from 0.87375 to 0.89701, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 34ms/step - accuracy: 0.8651 - loss: 0.4377 - val_accuracy: 0.8970 - val_loss: 0.3981
Epoch 43/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.8723 - loss: 0.3838
Epoch 43: val_accuracy did not improve from 0.89701
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step - accuracy: 0.8720 - loss: 0.3844 - val_accuracy: 0.8372 - val_loss: 0.4780
Epoch 44/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.8804 - loss: 0.3812
Epoch 44: val_accuracy did not improve from 0.89701
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.8806 - loss: 0.3808 - val_accuracy: 0.8804 - val_loss: 0.4181
Epoch 45/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9043 - loss: 0.3032
Epoch 45: val_accuracy did not improve from 0.89701
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9046 - loss: 0.3025 - val_accuracy: 0.8937 - val_loss: 0.3600
Epoch 46/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9154 - loss: 0.2622
Epoch 46: val_accuracy did not improve from 0.89701
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.9146 - loss: 0.2636 - val_accuracy: 0.8771 - val_loss: 0.3994
Epoch 47/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 39ms/step - accuracy: 0.8893 - loss: 0.3219
Epoch 47: val_accuracy did not improve from 0.89701
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 42ms/step - accuracy: 0.8894 - loss: 0.3221 - val_accuracy: 0.8970 - val_loss: 0.3841
Epoch 48/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9121 - loss: 0.2586
Epoch 48: val_accuracy did not improve from 0.89701
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - accuracy: 0.9120 - loss: 0.2590 - val_accuracy: 0.8538 - val_loss: 0.4477
Epoch 49/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9087 - loss: 0.2678
Epoch 49: val_accuracy did not improve from 0.89701
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9088 - loss: 0.2677 - val_accuracy: 0.8837 - val_loss: 0.3647
Epoch 50/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 63ms/step - accuracy: 0.9027 - loss: 0.2903
Epoch 50: val_accuracy improved from 0.89701 to 0.90698, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 3s 68ms/step - accuracy: 0.9033 - loss: 0.2886 - val_accuracy: 0.9070 - val_loss: 0.3580
Epoch 51/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9190 - loss: 0.2358
Epoch 51: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 33ms/step - accuracy: 0.9192 - loss: 0.2360 - val_accuracy: 0.9037 - val_loss: 0.3557
Epoch 52/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9312 - loss: 0.2245
Epoch 52: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.9310 - loss: 0.2245 - val_accuracy: 0.8804 - val_loss: 0.4021
Epoch 53/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9295 - loss: 0.2115
Epoch 53: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9295 - loss: 0.2123 - val_accuracy: 0.8970 - val_loss: 0.3566
Epoch 54/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9319 - loss: 0.2107
Epoch 54: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9311 - loss: 0.2127 - val_accuracy: 0.8738 - val_loss: 0.4039
Epoch 55/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9231 - loss: 0.2174
Epoch 55: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9233 - loss: 0.2177 - val_accuracy: 0.8704 - val_loss: 0.4486
Epoch 56/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9256 - loss: 0.2229
Epoch 56: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.9252 - loss: 0.2234 - val_accuracy: 0.8738 - val_loss: 0.4370
Epoch 57/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9092 - loss: 0.2566
Epoch 57: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9100 - loss: 0.2553 - val_accuracy: 0.8837 - val_loss: 0.4212
Epoch 58/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9453 - loss: 0.1952
Epoch 58: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9449 - loss: 0.1961 - val_accuracy: 0.9037 - val_loss: 0.3524
Epoch 59/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9438 - loss: 0.1910
Epoch 59: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9434 - loss: 0.1920 - val_accuracy: 0.8937 - val_loss: 0.4097
Epoch 60/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.9197 - loss: 0.2270
Epoch 60: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step - accuracy: 0.9199 - loss: 0.2267 - val_accuracy: 0.9003 - val_loss: 0.3554
Epoch 61/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step - accuracy: 0.9358 - loss: 0.1898
Epoch 61: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 33ms/step - accuracy: 0.9362 - loss: 0.1895 - val_accuracy: 0.8837 - val_loss: 0.4190
Epoch 62/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9336 - loss: 0.1988
Epoch 62: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - accuracy: 0.9339 - loss: 0.1990 - val_accuracy: 0.8638 - val_loss: 0.5222
Epoch 63/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9181 - loss: 0.2436
Epoch 63: val_accuracy did not improve from 0.90698
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9193 - loss: 0.2408 - val_accuracy: 0.9037 - val_loss: 0.3396
Epoch 64/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9394 - loss: 0.1948
Epoch 64: val_accuracy improved from 0.90698 to 0.92027, saving model to best_BiLSTM_model.keras
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step - accuracy: 0.9395 - loss: 0.1940 - val_accuracy: 0.9203 - val_loss: 0.3429
Epoch 65/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9438 - loss: 0.1651
Epoch 65: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9439 - loss: 0.1653 - val_accuracy: 0.9070 - val_loss: 0.3629
Epoch 66/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9549 - loss: 0.1450
Epoch 66: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9544 - loss: 0.1460 - val_accuracy: 0.9003 - val_loss: 0.3703
Epoch 67/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9548 - loss: 0.1369
Epoch 67: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9539 - loss: 0.1389 - val_accuracy: 0.8904 - val_loss: 0.3718
Epoch 68/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9401 - loss: 0.2001
Epoch 68: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9399 - loss: 0.2011 - val_accuracy: 0.8771 - val_loss: 0.3998
Epoch 69/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9033 - loss: 0.2848
Epoch 69: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9037 - loss: 0.2833 - val_accuracy: 0.8870 - val_loss: 0.3951
Epoch 70/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - accuracy: 0.9450 - loss: 0.1685
Epoch 70: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9449 - loss: 0.1687 - val_accuracy: 0.9136 - val_loss: 0.3539
Epoch 71/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step - accuracy: 0.9513 - loss: 0.1532
Epoch 71: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 32ms/step - accuracy: 0.9513 - loss: 0.1527 - val_accuracy: 0.9070 - val_loss: 0.3303
Epoch 72/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 29ms/step - accuracy: 0.9689 - loss: 0.1059
Epoch 72: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 33ms/step - accuracy: 0.9685 - loss: 0.1069 - val_accuracy: 0.9070 - val_loss: 0.3755
Epoch 73/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9624 - loss: 0.1135
Epoch 73: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - accuracy: 0.9623 - loss: 0.1141 - val_accuracy: 0.9037 - val_loss: 0.3714
Epoch 74/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9623 - loss: 0.0999
Epoch 74: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9622 - loss: 0.1003 - val_accuracy: 0.8904 - val_loss: 0.4280
Epoch 75/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9667 - loss: 0.0984
Epoch 75: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9665 - loss: 0.0995 - val_accuracy: 0.9003 - val_loss: 0.3786
Epoch 76/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9453 - loss: 0.1579
Epoch 76: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9454 - loss: 0.1573 - val_accuracy: 0.8970 - val_loss: 0.3694
Epoch 77/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9614 - loss: 0.1130
Epoch 77: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9612 - loss: 0.1136 - val_accuracy: 0.9203 - val_loss: 0.3363
Epoch 78/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9543 - loss: 0.1395
Epoch 78: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9543 - loss: 0.1392 - val_accuracy: 0.9169 - val_loss: 0.3774
Epoch 79/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9286 - loss: 0.2105
Epoch 79: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9287 - loss: 0.2103 - val_accuracy: 0.9003 - val_loss: 0.3719
Epoch 80/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step - accuracy: 0.9412 - loss: 0.1683
Epoch 80: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9414 - loss: 0.1677 - val_accuracy: 0.9169 - val_loss: 0.3300
Epoch 81/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.9714 - loss: 0.0882
Epoch 81: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 31ms/step - accuracy: 0.9709 - loss: 0.0891 - val_accuracy: 0.8870 - val_loss: 0.4329
Epoch 82/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 30ms/step - accuracy: 0.9644 - loss: 0.1141
Epoch 82: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 34ms/step - accuracy: 0.9646 - loss: 0.1134 - val_accuracy: 0.8904 - val_loss: 0.3723
Epoch 83/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9736 - loss: 0.0904
Epoch 83: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - accuracy: 0.9735 - loss: 0.0908 - val_accuracy: 0.9103 - val_loss: 0.4140
Epoch 84/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9689 - loss: 0.0970
Epoch 84: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9682 - loss: 0.0990 - val_accuracy: 0.8771 - val_loss: 0.5181
Epoch 85/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9528 - loss: 0.1224
Epoch 85: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9528 - loss: 0.1229 - val_accuracy: 0.8970 - val_loss: 0.3900
Epoch 86/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9661 - loss: 0.1087
Epoch 86: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9660 - loss: 0.1083 - val_accuracy: 0.9136 - val_loss: 0.3837
Epoch 87/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9626 - loss: 0.0942
Epoch 87: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9629 - loss: 0.0937 - val_accuracy: 0.8937 - val_loss: 0.5120
Epoch 88/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9683 - loss: 0.0803
Epoch 88: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9686 - loss: 0.0801 - val_accuracy: 0.9169 - val_loss: 0.4091
Epoch 89/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9670 - loss: 0.0953
Epoch 89: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step - accuracy: 0.9670 - loss: 0.0958 - val_accuracy: 0.9037 - val_loss: 0.3886
Epoch 90/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9407 - loss: 0.1855
Epoch 90: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9408 - loss: 0.1844 - val_accuracy: 0.8837 - val_loss: 0.4677
Epoch 91/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step - accuracy: 0.9248 - loss: 0.2015
Epoch 91: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step - accuracy: 0.9244 - loss: 0.2033 - val_accuracy: 0.8538 - val_loss: 0.5512
Epoch 92/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.9159 - loss: 0.2408
Epoch 92: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 31ms/step - accuracy: 0.9165 - loss: 0.2393 - val_accuracy: 0.8970 - val_loss: 0.4476
Epoch 93/100
38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 28ms/step - accuracy: 0.9727 - loss: 0.0831
Epoch 93: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 32ms/step - accuracy: 0.9725 - loss: 0.0840 - val_accuracy: 0.9037 - val_loss: 0.4595
Epoch 94/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 27ms/step - accuracy: 0.9598 - loss: 0.1115
Epoch 94: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step - accuracy: 0.9596 - loss: 0.1117 - val_accuracy: 0.8937 - val_loss: 0.4567
Epoch 95/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9601 - loss: 0.1126
Epoch 95: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9600 - loss: 0.1134 - val_accuracy: 0.8671 - val_loss: 0.4874
Epoch 96/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9495 - loss: 0.1718
Epoch 96: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9504 - loss: 0.1688 - val_accuracy: 0.9203 - val_loss: 0.3672
Epoch 97/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9824 - loss: 0.0550
Epoch 97: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9824 - loss: 0.0554 - val_accuracy: 0.9003 - val_loss: 0.4744
Epoch 98/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 21ms/step - accuracy: 0.9817 - loss: 0.0630
Epoch 98: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9819 - loss: 0.0626 - val_accuracy: 0.8970 - val_loss: 0.4345
Epoch 99/100
37/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9912 - loss: 0.0371
Epoch 99: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9910 - loss: 0.0376 - val_accuracy: 0.8904 - val_loss: 0.5079
Epoch 100/100
36/38 ━━━━━━━━━━━━━━━━━━━━ 0s 20ms/step - accuracy: 0.9918 - loss: 0.0400
Epoch 100: val_accuracy did not improve from 0.92027
38/38 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step - accuracy: 0.9915 - loss: 0.0407 - val_accuracy: 0.9070 - val_loss: 0.4552


# Print model summary
model_Bi_LSTM.summary()

Model: "sequential_2"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ embedding_2 (Embedding)              │ (None, 110, 100)            │         365,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bidirectional_2 (Bidirectional)      │ (None, 110, 256)            │         234,496 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_2 (Dropout)                  │ (None, 110, 256)            │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ bidirectional_3 (Bidirectional)      │ (None, 128)                 │         164,352 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout_3 (Dropout)                  │ (None, 128)                 │               0 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_2 (Dense)                      │ (None, 5)                   │             645 │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 1,564,281 (5.97 MB)

 Trainable params: 399,493 (1.52 MB)

 Non-trainable params: 365,800 (1.40 MB)

 Optimizer params: 798,988 (3.05 MB)


#from keras.utils.vis_utils import plot_model
from tensorflow.keras.utils import plot_model

# Assuming you have a defined model named 'model'
plot_model(model_Bi_LSTM, to_file='model_plot.png', show_shapes=True, show_layer_names=True, dpi=100)


# prompt: decrease the size of model plot

#from keras.utils.vis_utils import plot_model

# Assuming you have a defined model named 'model'
plot_model(model_Bi_LSTM, to_file='model_plot.png', show_shapes=True, show_layer_names=True, dpi=300)


train_loss, train_accuracy = model.evaluate(X_train, y_train)
print(f"Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}")

test_loss, test_accuracy = model.evaluate(X_test, y_test)
print(f"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}")

evaluate_model_performance(model_Bi_LSTM, X_test, y_test)

38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9550 - loss: 0.1512
Train Loss: 0.1621, Train Accuracy: 0.9476
10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step - accuracy: 0.9106 - loss: 0.3092 
Test Loss: 0.3235, Test Accuracy: 0.9003
10/10 ━━━━━━━━━━━━━━━━━━━━ 1s 43ms/step

              precision    recall  f1-score   support

           I       0.78      0.86      0.82        63
          II       0.93      0.88      0.91        60
         III       0.93      0.92      0.93        61
          IV       0.92      0.93      0.93        60
           V       1.00      0.95      0.97        57

    accuracy                           0.91       301
   macro avg       0.91      0.91      0.91       301
weighted avg       0.91      0.91      0.91       301


y_pred_bi = model_Bi_LSTM.predict(X_test)
F1_score= f1_score(np.argmax(y_test, axis=1), np.argmax(y_pred_bi, axis=1), average='weighted')
print(f"F1 Score: {F1_score:.4f}")
tempResult1 = pd.DataFrame({'Data Description':"BiDirectional LSTM on W2V using augmented data",
                                'Model Description':"LSTM on W2V dataset",
                                'Train Accuracy':round(train_accuracy*100,2),
                                'Test Accuracy':round(test_accuracy*100,2),
                                'F1 Score':round(F1_score*100,2),
                                }, index=[0])

10/10 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step
F1 Score: 0.9082


combinedNNModelResult = pd.concat([tempResult, tempResult1])
pd.set_option('display.width',1500)
print(f'Collating Results : \n-----------------------\n{combinedNNModelResult}\n')

Collating Results : 
-----------------------
                                 Data Description               Model Description  Train Accuracy  Test Accuracy  F1 Score
0                LSTM on W2V using augmented data  LSTM on W2V-augmented dataset            94.09          91.69     90.20
0  BiDirectional LSTM on W2V using augmented data             LSTM on W2V dataset           94.76          90.03     90.82


combinedNNModelResult


pip install nlpaug

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Installing collected packages: nlpaug
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!pip install tensorflow-text

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Installing collected packages: tensorflow-text
Successfully installed tensorflow-text-2.17.0


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import random
import re
import time
import warnings
import pickle
import os
import string

import tensorflow as tf

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from gensim.models import Word2Vec, KeyedVectors
from gensim.scripts.glove2word2vec import glove2word2vec

from wordcloud import WordCloud, ImageColorGenerator

import nlpaug.augmenter.char as nac
import nlpaug.augmenter.word as naw
import nlpaug.augmenter.sentence as nas
import nlpaug.flow as naf

from nlpaug.util import Action

from google.cloud import storage
import tensorflow_hub as hub
import tensorflow_text as text

# Ensure warnings are ignored
warnings.filterwarnings("ignore")


df = pd.read_excel('drive/My Drive/AIML/Data+Set+-+industrial_safety_and_health_database_with_accidents_description.xlsx')


df = df.replace('\n', '', regex=True)
df['Desc_count'] = df['Description'].apply(lambda x: len(x))


df.drop(['Unnamed: 0', 'Data', 'Countries', 'Local', 'Industry Sector','Potential Accident Level', 'Genre', 'Employee or Third Party', 'Critical Risk', 'Desc_count'], axis=1, inplace=True)


df.dropna(inplace=True)


df.duplicated().sum()

12


#delete duplicates
df.drop_duplicates(inplace=True)
#reset index
df.reset_index(drop=True, inplace=True)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 413 entries, 0 to 412
Data columns (total 2 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Accident Level  413 non-null    object
 1   Description     413 non-null    object
dtypes: object(2)
memory usage: 6.6+ KB


X = df.drop(columns=['Accident Level'])
y = df['Accident Level']


#splitting dataset and augmenting to train data
X_train_ini, X_test_ini, y_train_ini, y_test_ini = train_test_split(X, y, test_size=0.20, stratify=y, random_state=42)


import pandas as pd

train_df = pd.concat([X_train_ini, y_train_ini], axis=1)

# reset the index to ensure it is sequential
train_df.reset_index(drop=True, inplace=True)

# Display the combined DataFrame
print(train_df.head())

                                         Description Accident Level
0  Collaborator was cleaning the sink of the Copp...              I
1  In circumstances that the Operator 176 was goi...              I
2  While preparing to mount polypropylene tubing,...             II
3  The employee reports that after carrying out a...              I
4  During the maintenance on the LX-P-205B pump, ...              I


df_ini = df.copy()


df_ini.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 413 entries, 0 to 412
Data columns (total 2 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Accident Level  413 non-null    object
 1   Description     413 non-null    object
dtypes: object(2)
memory usage: 6.6+ KB


df = train_df.copy()
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 330 entries, 0 to 329
Data columns (total 2 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Description     330 non-null    object
 1   Accident Level  330 non-null    object
dtypes: object(2)
memory usage: 5.3+ KB


df['Accident Level'].value_counts()


# Creating Separate dataframes as per Potential Accident level.
Pal_I   = df[df['Accident Level'] == 'I']
Pal_II  = df[df['Accident Level'] == 'II']
Pal_III = df[df['Accident Level'] == 'III']
Pal_IV  = df[df['Accident Level'] == 'IV']
Pal_V   = df[df['Accident Level'] == 'V']


from gensim.scripts.glove2word2vec import glove2word2vec
glove_input_file = '/content/drive/My Drive/Colab Notebooks/glove.6B.100d.txt'
word2vec_output_file = 'glove.6B.100d.txt.word2vec'
glove2word2vec(glove_input_file, word2vec_output_file)

(400000, 100)


from gensim.models import KeyedVectors
# load the Stanford GloVe model
filename = 'glove.6B.100d.txt.word2vec'
glove_model = KeyedVectors.load_word2vec_format(filename, binary=False)


# Save the Word2Vec model for later use (if necessary)
glove_model.save_word2vec_format('glove.6B.100d.txt.word2vec', binary=True)


import nlpaug.augmenter.word as naw

# Load the correctly formatted Word2Vec model with NLPAug
aug_w2v = naw.WordEmbsAug(model_type='word2vec', model_path='glove.6B.100d.txt.word2vec', action="substitute")


# Augment  records for Accident Level
Pal_II_Sample_Df  = Pal_II.sample(n=210, replace=True)
Pal_III_Sample_Df = Pal_III.sample(n=215, replace=True)
Pal_IV_Sample_Df  = Pal_IV.sample(n=216, replace=True)
Pal_V_Sample_Df   = Pal_V.sample(n=235, replace=True)


# Function to actually augment the description data.
def augmentText(pal_df):
    for i, row in pal_df.iterrows():
        pal_df.at[i, 'Description'] = aug_w2v.augment(row['Description'])


# Invoke augmentText function

augmentText(Pal_II_Sample_Df)
augmentText(Pal_III_Sample_Df)
augmentText(Pal_IV_Sample_Df)
augmentText(Pal_V_Sample_Df)


# Concatenating Augmented dataframes.
frames = [df, Pal_IV_Sample_Df, Pal_II_Sample_Df, Pal_III_Sample_Df, Pal_V_Sample_Df]
df_bal = pd.concat(frames)

# Shuffling the records in dataframe as they do remain grouped after concatenation.
df_bal = df_bal.sample(frac = 1).reset_index(drop=True)


df_bal.shape

(1206, 2)


df_bal['Accident Level'].value_counts()


df_nlp=df_bal.copy()


# defining a function to remove special character

import pandas as pd
import re
import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')

def remove_special_characters(text):
    if isinstance(text, str):
        pattern = '[^A-Za-z0-9]+'
        new_text = re.sub(pattern, ' ', text)
        return new_text
    else:
        return text

def remove_stopwords(text):
    if isinstance(text, str):
        words = text.split()
        stop_words = set(stopwords.words('english'))
        new_text = ' '.join([word for word in words if word.lower() not in stop_words])
        return new_text
    else:
        return text

[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!


import nltk
nltk.data.path = []  # Clear existing paths
nltk.download('wordnet')
nltk.download('omw-1.4')
nltk.download('punkt')

import spacy
import nltk
from nltk.stem import WordNetLemmatizer

# Initialize NLTK resources
nltk.download('wordnet')
nltk.download('omw-1.4')

# Define NLTK lemmatizer
wnl = WordNetLemmatizer()

# Load SpaCy model
try:
    nlp = spacy.load('en_core_web_sm')
    spacy_available = True
except Exception as e:
    print(f"SpaCy model load error: {e}")
    spacy_available = False

# Define lemmatization function
def apply_lemmatizer(text):
    if isinstance(text, list):
        text = ' '.join(text)  # Convert list back to string

    if spacy_available:
        try:
            doc = nlp(text)
            return ' '.join([token.lemma_ for token in doc])
        except Exception as e:
            print(f"SpaCy lemmatization error: {e}")
            # Fallback to NLTK
            return nltk_lemmatize(text)
    else:
        # Fallback to NLTK if SpaCy is not available
        return nltk_lemmatize(text)

def nltk_lemmatize(text):
    words = text.split()
    return ' '.join([wnl.lemmatize(word) for word in words])

[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!


# Ensure that df_bal has been properly defined
df_bal['cleaned_text'] = df_bal['Description'].apply(remove_special_characters)

# If you need to apply the transformation to df_nlp, make sure df_nlp has the 'Description' column
df_nlp = df_bal.copy()  # Assuming you want to work with df_nlp as a copy of df_bal

# Apply additional transformations to df_nlp
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].str.lower()
# Removing extra whitespaces from the text
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].str.strip()
# Removing extra whitespaces from the text
df_nlp['cleaned_text'] = df_nlp['cleaned_text'].replace(to_replace=r'\d', value='', regex=True)
df_nlp['cleaned_text_without_stopwords'] = df_nlp['cleaned_text'].apply(remove_stopwords)
#Tokenize sentence into words
df_nlp['cleaned_text_without_stopwords'] = df_nlp['cleaned_text_without_stopwords'].astype(str)
df_nlp['cleaned_text_without_stopwords'] = df_nlp['cleaned_text_without_stopwords'].fillna('')

nltk.data.path.clear()
nltk.data.path.append('/root/nltk_data')
nltk.download('punkt', download_dir='/root/nltk_data')
import nltk
from nltk.tokenize import word_tokenize
df_nlp['cleaned_text_stopwords_token'] = df_nlp['cleaned_text_without_stopwords'].apply(lambda x: word_tokenize(x) if isinstance(x, str) else [])

# Apply the function to the DataFrame
df_nlp['final_cleaned_text'] = df_nlp['cleaned_text_stopwords_token'].apply(apply_lemmatizer)

[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!


df_nlp.head(1)


df_nlp_final_cleaned_text = df_nlp.copy()


#W2V
# creating a list of all words in our data
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences


words_list = [item.split(" ") for item in df_nlp['final_cleaned_text'].values]

# Assuming words_list is your list of sentences (a list of lists of words)
tokenizer = Tokenizer()
tokenizer.fit_on_texts(words_list)

# Get the word index from the tokenizer
word_index = tokenizer.word_index

sequences = tokenizer.texts_to_sequences(words_list)

# Calculate sentence lengths
sentence_lengths = [len(seq) for seq in sequences]
max_sentence_length = max(sentence_lengths)


# Pad sequences to ensure uniform length
X_padded = pad_sequences(sequences, maxlen=max_sentence_length)

print("Padded sequences shape:", X_padded.shape)

# creating an instance of Word2Vec
model_W2V = Word2Vec(words_list, vector_size = 100, min_count = 1, window=5, workers = 6)
# Checking the size of the vocabulary
print("Length of the vocabulary is", len(list(model_W2V.wv.key_to_index)))

# Dictionary with key as words and the value as the embedding vector.

words = list(model_W2V.wv.key_to_index.keys())

# Retrieving word vectors for all the words present in the model's vocabulary
wvs = model_W2V.wv[words].tolist()

# Creating a dictionary of words and their corresponding vectors
word_vector_dict = dict(zip(words, wvs))

import numpy as np

# Define the size of the embedding
embedding_dim = 100  # This should match the vector_size used in Word2Vec
vocab_size = len(word_index) + 1  # +1 because Keras word indices are 1-based

# Initialize embedding matrix with zeros
embedding_matrix = np.zeros((vocab_size, embedding_dim))

# Fill the embedding matrix with Word2Vec vectors
for word, i in word_index.items():
    if word in word_vector_dict:  # Check if the word exists in the Word2Vec model
        embedding_matrix[i] = word_vector_dict[word]

Padded sequences shape: (1206, 109)
Length of the vocabulary is 3217


print (vocab_size)
print (embedding_matrix.shape)
print (max_sentence_length)
print (embedding_dim)

3218
(3218, 100)
109
100


y = df_bal['Accident Level']


!pip install scikit-learn  # Install scikit-learn if you haven't already
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse_output=False)  # Create OneHotEncoder instance
y_encoded = encoder.fit_transform(y.values.reshape(-1, 1))  # Reshape and encode

Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.3.2)
Requirement already satisfied: numpy<2.0,>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)
Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)
Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)
Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)


X_train, X_test, y_train, y_test = train_test_split(X_padded, y_encoded, test_size=0.2,stratify=y, random_state=42)


from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger

# Define the model
model = Sequential()

# Create the embedding layer
embedding_layer = Embedding(input_dim=vocab_size,
                            output_dim=embedding_dim,
                            weights=[embedding_matrix],
                            input_length=max_sentence_length,  # sentence_length is the length of input sequences
                            trainable=False)  # Set trainable to False to use the embeddings as they are
model.add(embedding_layer)

# Add other layers (LSTM, Dense, etc.)
model.add(LSTM(128, dropout=0.3, return_sequences=True))
model.add(LSTM(64, dropout=0.3))
model.add(Dense(5, activation='softmax'))

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Model callbacks
callbacks = [
    ModelCheckpoint('best_model_lstm.keras', save_best_only=True, monitor='val_accuracy', mode='max', verbose=1),
    CSVLogger('training_log.csv')
]

# Display model summary
model.summary()

Model: "sequential"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ Layer (type)                         ┃ Output Shape                ┃         Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ embedding (Embedding)                │ ?                           │         321,800 │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm (LSTM)                          │ ?                           │     0 (unbuilt) │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_1 (LSTM)                        │ ?                           │     0 (unbuilt) │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense (Dense)                        │ ?                           │     0 (unbuilt) │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘

 Total params: 321,800 (1.23 MB)

 Trainable params: 0 (0.00 B)

 Non-trainable params: 321,800 (1.23 MB)


history_lstm=model.fit(X_train, y_train, epochs=80, batch_size=32, validation_data=(X_test, y_test),callbacks=callbacks)

Epoch 1/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step - accuracy: 0.2302 - loss: 1.6064
Epoch 1: val_accuracy improved from -inf to 0.20661, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 22s 398ms/step - accuracy: 0.2308 - loss: 1.6058 - val_accuracy: 0.2066 - val_loss: 1.5455
Epoch 2/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 282ms/step - accuracy: 0.2864 - loss: 1.5248
Epoch 2: val_accuracy improved from 0.20661 to 0.41736, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 17s 301ms/step - accuracy: 0.2872 - loss: 1.5238 - val_accuracy: 0.4174 - val_loss: 1.3357
Epoch 3/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 230ms/step - accuracy: 0.3251 - loss: 1.4603
Epoch 3: val_accuracy did not improve from 0.41736
31/31 ━━━━━━━━━━━━━━━━━━━━ 8s 274ms/step - accuracy: 0.3265 - loss: 1.4586 - val_accuracy: 0.3264 - val_loss: 1.4428
Epoch 4/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 205ms/step - accuracy: 0.4205 - loss: 1.3214
Epoch 4: val_accuracy improved from 0.41736 to 0.55372, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 225ms/step - accuracy: 0.4198 - loss: 1.3209 - val_accuracy: 0.5537 - val_loss: 1.0559
Epoch 5/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 290ms/step - accuracy: 0.4261 - loss: 1.2298
Epoch 5: val_accuracy did not improve from 0.55372
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 309ms/step - accuracy: 0.4275 - loss: 1.2282 - val_accuracy: 0.4628 - val_loss: 1.2677
Epoch 6/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 242ms/step - accuracy: 0.4550 - loss: 1.2358
Epoch 6: val_accuracy improved from 0.55372 to 0.57025, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 278ms/step - accuracy: 0.4548 - loss: 1.2356 - val_accuracy: 0.5702 - val_loss: 1.0387
Epoch 7/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 205ms/step - accuracy: 0.5109 - loss: 1.1391
Epoch 7: val_accuracy did not improve from 0.57025
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 223ms/step - accuracy: 0.5106 - loss: 1.1382 - val_accuracy: 0.5413 - val_loss: 0.9518
Epoch 8/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 275ms/step - accuracy: 0.5299 - loss: 1.0508
Epoch 8: val_accuracy improved from 0.57025 to 0.60331, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 298ms/step - accuracy: 0.5301 - loss: 1.0509 - val_accuracy: 0.6033 - val_loss: 0.9211
Epoch 9/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 269ms/step - accuracy: 0.5393 - loss: 1.0297
Epoch 9: val_accuracy did not improve from 0.60331
31/31 ━━━━━━━━━━━━━━━━━━━━ 11s 313ms/step - accuracy: 0.5392 - loss: 1.0299 - val_accuracy: 0.5661 - val_loss: 1.0873
Epoch 10/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 206ms/step - accuracy: 0.5469 - loss: 1.0399
Epoch 10: val_accuracy did not improve from 0.60331
31/31 ━━━━━━━━━━━━━━━━━━━━ 7s 224ms/step - accuracy: 0.5466 - loss: 1.0399 - val_accuracy: 0.5785 - val_loss: 0.9765
Epoch 11/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step - accuracy: 0.5633 - loss: 1.0278
Epoch 11: val_accuracy did not improve from 0.60331
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 325ms/step - accuracy: 0.5633 - loss: 1.0273 - val_accuracy: 0.5579 - val_loss: 0.9030
Epoch 12/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 208ms/step - accuracy: 0.5444 - loss: 0.9819
Epoch 12: val_accuracy did not improve from 0.60331
31/31 ━━━━━━━━━━━━━━━━━━━━ 7s 227ms/step - accuracy: 0.5445 - loss: 0.9820 - val_accuracy: 0.5868 - val_loss: 0.8832
Epoch 13/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step - accuracy: 0.5797 - loss: 0.9865
Epoch 13: val_accuracy did not improve from 0.60331
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 306ms/step - accuracy: 0.5788 - loss: 0.9877 - val_accuracy: 0.5537 - val_loss: 0.9678
Epoch 14/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 241ms/step - accuracy: 0.5801 - loss: 0.9990
Epoch 14: val_accuracy improved from 0.60331 to 0.62810, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 277ms/step - accuracy: 0.5804 - loss: 0.9979 - val_accuracy: 0.6281 - val_loss: 0.8662
Epoch 15/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 210ms/step - accuracy: 0.5640 - loss: 1.0076
Epoch 15: val_accuracy did not improve from 0.62810
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 228ms/step - accuracy: 0.5640 - loss: 1.0068 - val_accuracy: 0.5826 - val_loss: 0.8778
Epoch 16/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 294ms/step - accuracy: 0.5491 - loss: 0.9930
Epoch 16: val_accuracy improved from 0.62810 to 0.63223, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 315ms/step - accuracy: 0.5500 - loss: 0.9916 - val_accuracy: 0.6322 - val_loss: 0.8489
Epoch 17/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - accuracy: 0.5996 - loss: 0.9349
Epoch 17: val_accuracy did not improve from 0.63223
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 294ms/step - accuracy: 0.5994 - loss: 0.9354 - val_accuracy: 0.6157 - val_loss: 0.8332
Epoch 18/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 216ms/step - accuracy: 0.6083 - loss: 0.9069
Epoch 18: val_accuracy improved from 0.63223 to 0.64463, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 8s 237ms/step - accuracy: 0.6080 - loss: 0.9081 - val_accuracy: 0.6446 - val_loss: 0.8682
Epoch 19/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step - accuracy: 0.6371 - loss: 0.9175
Epoch 19: val_accuracy did not improve from 0.64463
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 308ms/step - accuracy: 0.6367 - loss: 0.9177 - val_accuracy: 0.6033 - val_loss: 0.8974
Epoch 20/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 257ms/step - accuracy: 0.6002 - loss: 0.9523
Epoch 20: val_accuracy did not improve from 0.64463
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 291ms/step - accuracy: 0.6008 - loss: 0.9515 - val_accuracy: 0.6364 - val_loss: 0.8457
Epoch 21/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 213ms/step - accuracy: 0.6367 - loss: 0.8972
Epoch 21: val_accuracy did not improve from 0.64463
31/31 ━━━━━━━━━━━━━━━━━━━━ 8s 231ms/step - accuracy: 0.6361 - loss: 0.8976 - val_accuracy: 0.6405 - val_loss: 0.8364
Epoch 22/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 295ms/step - accuracy: 0.6249 - loss: 0.8966
Epoch 22: val_accuracy did not improve from 0.64463
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 314ms/step - accuracy: 0.6253 - loss: 0.8962 - val_accuracy: 0.6446 - val_loss: 0.7959
Epoch 23/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 252ms/step - accuracy: 0.6189 - loss: 0.8897
Epoch 23: val_accuracy improved from 0.64463 to 0.65702, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 298ms/step - accuracy: 0.6191 - loss: 0.8897 - val_accuracy: 0.6570 - val_loss: 0.7739
Epoch 24/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 236ms/step - accuracy: 0.6304 - loss: 0.9028
Epoch 24: val_accuracy improved from 0.65702 to 0.67355, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 8s 257ms/step - accuracy: 0.6307 - loss: 0.9023 - val_accuracy: 0.6736 - val_loss: 0.7697
Epoch 25/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 301ms/step - accuracy: 0.6637 - loss: 0.8334
Epoch 25: val_accuracy did not improve from 0.67355
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 324ms/step - accuracy: 0.6631 - loss: 0.8336 - val_accuracy: 0.6570 - val_loss: 0.7729
Epoch 26/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 212ms/step - accuracy: 0.6910 - loss: 0.7670
Epoch 26: val_accuracy improved from 0.67355 to 0.68182, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 7s 233ms/step - accuracy: 0.6901 - loss: 0.7687 - val_accuracy: 0.6818 - val_loss: 0.7271
Epoch 27/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step - accuracy: 0.6670 - loss: 0.8092
Epoch 27: val_accuracy improved from 0.68182 to 0.69421, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 337ms/step - accuracy: 0.6671 - loss: 0.8087 - val_accuracy: 0.6942 - val_loss: 0.6794
Epoch 28/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 268ms/step - accuracy: 0.6945 - loss: 0.7529
Epoch 28: val_accuracy did not improve from 0.69421
31/31 ━━━━━━━━━━━━━━━━━━━━ 19s 290ms/step - accuracy: 0.6943 - loss: 0.7538 - val_accuracy: 0.6942 - val_loss: 0.7383
Epoch 29/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 292ms/step - accuracy: 0.7080 - loss: 0.7766
Epoch 29: val_accuracy improved from 0.69421 to 0.70661, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 11s 337ms/step - accuracy: 0.7073 - loss: 0.7772 - val_accuracy: 0.7066 - val_loss: 0.6255
Epoch 30/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 314ms/step - accuracy: 0.7177 - loss: 0.7326
Epoch 30: val_accuracy improved from 0.70661 to 0.71074, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 21s 363ms/step - accuracy: 0.7174 - loss: 0.7329 - val_accuracy: 0.7107 - val_loss: 0.6346
Epoch 31/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 416ms/step - accuracy: 0.7260 - loss: 0.7216
Epoch 31: val_accuracy improved from 0.71074 to 0.76860, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 25s 509ms/step - accuracy: 0.7254 - loss: 0.7223 - val_accuracy: 0.7686 - val_loss: 0.5995
Epoch 32/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 441ms/step - accuracy: 0.7327 - loss: 0.6754
Epoch 32: val_accuracy did not improve from 0.76860
31/31 ━━━━━━━━━━━━━━━━━━━━ 20s 492ms/step - accuracy: 0.7329 - loss: 0.6757 - val_accuracy: 0.7025 - val_loss: 0.6861
Epoch 33/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 244ms/step - accuracy: 0.6357 - loss: 0.8793
Epoch 33: val_accuracy did not improve from 0.76860
31/31 ━━━━━━━━━━━━━━━━━━━━ 14s 288ms/step - accuracy: 0.6353 - loss: 0.8804 - val_accuracy: 0.6736 - val_loss: 0.7644
Epoch 34/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 252ms/step - accuracy: 0.6846 - loss: 0.8017
Epoch 34: val_accuracy did not improve from 0.76860
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 269ms/step - accuracy: 0.6852 - loss: 0.8007 - val_accuracy: 0.7438 - val_loss: 0.6280
Epoch 35/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 322ms/step - accuracy: 0.7276 - loss: 0.6842
Epoch 35: val_accuracy improved from 0.76860 to 0.77273, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 12s 342ms/step - accuracy: 0.7274 - loss: 0.6848 - val_accuracy: 0.7727 - val_loss: 0.6036
Epoch 36/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 400ms/step - accuracy: 0.7514 - loss: 0.6472
Epoch 36: val_accuracy did not improve from 0.77273
31/31 ━━━━━━━━━━━━━━━━━━━━ 23s 431ms/step - accuracy: 0.7508 - loss: 0.6487 - val_accuracy: 0.7603 - val_loss: 0.6107
Epoch 37/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 380ms/step - accuracy: 0.7326 - loss: 0.6758
Epoch 37: val_accuracy improved from 0.77273 to 0.80579, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 22s 471ms/step - accuracy: 0.7326 - loss: 0.6765 - val_accuracy: 0.8058 - val_loss: 0.5563
Epoch 38/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 388ms/step - accuracy: 0.7693 - loss: 0.6183
Epoch 38: val_accuracy did not improve from 0.80579
31/31 ━━━━━━━━━━━━━━━━━━━━ 19s 406ms/step - accuracy: 0.7687 - loss: 0.6187 - val_accuracy: 0.7686 - val_loss: 0.5390
Epoch 39/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 267ms/step - accuracy: 0.7482 - loss: 0.6590
Epoch 39: val_accuracy improved from 0.80579 to 0.83471, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 17s 287ms/step - accuracy: 0.7486 - loss: 0.6582 - val_accuracy: 0.8347 - val_loss: 0.4711
Epoch 40/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 299ms/step - accuracy: 0.7824 - loss: 0.5691
Epoch 40: val_accuracy improved from 0.83471 to 0.85950, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 12s 344ms/step - accuracy: 0.7822 - loss: 0.5693 - val_accuracy: 0.8595 - val_loss: 0.4506
Epoch 41/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 320ms/step - accuracy: 0.7632 - loss: 0.6016
Epoch 41: val_accuracy did not improve from 0.85950
31/31 ━━━━━━━━━━━━━━━━━━━━ 20s 339ms/step - accuracy: 0.7637 - loss: 0.6010 - val_accuracy: 0.8347 - val_loss: 0.4632
Epoch 42/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 218ms/step - accuracy: 0.7888 - loss: 0.5519
Epoch 42: val_accuracy did not improve from 0.85950
31/31 ━━━━━━━━━━━━━━━━━━━━ 8s 263ms/step - accuracy: 0.7890 - loss: 0.5516 - val_accuracy: 0.7851 - val_loss: 0.4990
Epoch 43/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 207ms/step - accuracy: 0.7899 - loss: 0.5846
Epoch 43: val_accuracy improved from 0.85950 to 0.87603, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 228ms/step - accuracy: 0.7908 - loss: 0.5833 - val_accuracy: 0.8760 - val_loss: 0.3976
Epoch 44/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 307ms/step - accuracy: 0.8225 - loss: 0.5044
Epoch 44: val_accuracy did not improve from 0.87603
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 326ms/step - accuracy: 0.8225 - loss: 0.5042 - val_accuracy: 0.8099 - val_loss: 0.5225
Epoch 45/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 240ms/step - accuracy: 0.7877 - loss: 0.5735
Epoch 45: val_accuracy did not improve from 0.87603
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 297ms/step - accuracy: 0.7885 - loss: 0.5718 - val_accuracy: 0.8719 - val_loss: 0.3824
Epoch 46/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 624ms/step - accuracy: 0.8179 - loss: 0.4753
Epoch 46: val_accuracy improved from 0.87603 to 0.90496, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 22s 671ms/step - accuracy: 0.8185 - loss: 0.4748 - val_accuracy: 0.9050 - val_loss: 0.3568
Epoch 47/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 542ms/step - accuracy: 0.8662 - loss: 0.4259
Epoch 47: val_accuracy did not improve from 0.90496
31/31 ━━━━━━━━━━━━━━━━━━━━ 17s 560ms/step - accuracy: 0.8660 - loss: 0.4264 - val_accuracy: 0.8719 - val_loss: 0.3916
Epoch 48/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 209ms/step - accuracy: 0.8509 - loss: 0.4403
Epoch 48: val_accuracy improved from 0.90496 to 0.91322, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 7s 230ms/step - accuracy: 0.8509 - loss: 0.4403 - val_accuracy: 0.9132 - val_loss: 0.3260
Epoch 49/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 267ms/step - accuracy: 0.8726 - loss: 0.3995
Epoch 49: val_accuracy did not improve from 0.91322
31/31 ━━━━━━━━━━━━━━━━━━━━ 12s 286ms/step - accuracy: 0.8724 - loss: 0.3999 - val_accuracy: 0.9050 - val_loss: 0.3166
Epoch 50/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 287ms/step - accuracy: 0.8472 - loss: 0.4121
Epoch 50: val_accuracy did not improve from 0.91322
31/31 ━━━━━━━━━━━━━━━━━━━━ 11s 330ms/step - accuracy: 0.8474 - loss: 0.4116 - val_accuracy: 0.9008 - val_loss: 0.3217
Epoch 51/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 211ms/step - accuracy: 0.8457 - loss: 0.4378
Epoch 51: val_accuracy did not improve from 0.91322
31/31 ━━━━━━━━━━━━━━━━━━━━ 7s 229ms/step - accuracy: 0.8457 - loss: 0.4382 - val_accuracy: 0.8471 - val_loss: 0.4742
Epoch 52/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 303ms/step - accuracy: 0.8653 - loss: 0.3979
Epoch 52: val_accuracy did not improve from 0.91322
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 321ms/step - accuracy: 0.8647 - loss: 0.3991 - val_accuracy: 0.8967 - val_loss: 0.3261
Epoch 53/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 217ms/step - accuracy: 0.8524 - loss: 0.4217
Epoch 53: val_accuracy did not improve from 0.91322
31/31 ━━━━━━━━━━━━━━━━━━━━ 8s 251ms/step - accuracy: 0.8526 - loss: 0.4213 - val_accuracy: 0.9008 - val_loss: 0.3199
Epoch 54/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 211ms/step - accuracy: 0.8816 - loss: 0.3389
Epoch 54: val_accuracy did not improve from 0.91322
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 230ms/step - accuracy: 0.8814 - loss: 0.3403 - val_accuracy: 0.9050 - val_loss: 0.3269
Epoch 55/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 321ms/step - accuracy: 0.8847 - loss: 0.3625
Epoch 55: val_accuracy did not improve from 0.91322
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 339ms/step - accuracy: 0.8848 - loss: 0.3620 - val_accuracy: 0.9050 - val_loss: 0.2916
Epoch 56/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 209ms/step - accuracy: 0.8989 - loss: 0.3194
Epoch 56: val_accuracy improved from 0.91322 to 0.92149, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 7s 228ms/step - accuracy: 0.8989 - loss: 0.3194 - val_accuracy: 0.9215 - val_loss: 0.2691
Epoch 57/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step - accuracy: 0.8964 - loss: 0.3003
Epoch 57: val_accuracy did not improve from 0.92149
31/31 ━━━━━━━━━━━━━━━━━━━━ 14s 332ms/step - accuracy: 0.8964 - loss: 0.3007 - val_accuracy: 0.9132 - val_loss: 0.2802
Epoch 58/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 211ms/step - accuracy: 0.9074 - loss: 0.3113
Epoch 58: val_accuracy improved from 0.92149 to 0.92975, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 17s 231ms/step - accuracy: 0.9074 - loss: 0.3110 - val_accuracy: 0.9298 - val_loss: 0.2504
Epoch 59/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 211ms/step - accuracy: 0.9031 - loss: 0.2902
Epoch 59: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 230ms/step - accuracy: 0.9029 - loss: 0.2905 - val_accuracy: 0.8802 - val_loss: 0.3000
Epoch 60/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 304ms/step - accuracy: 0.8790 - loss: 0.3394
Epoch 60: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 322ms/step - accuracy: 0.8794 - loss: 0.3388 - val_accuracy: 0.8843 - val_loss: 0.3203
Epoch 61/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 207ms/step - accuracy: 0.8900 - loss: 0.3117
Epoch 61: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 7s 225ms/step - accuracy: 0.8904 - loss: 0.3113 - val_accuracy: 0.9091 - val_loss: 0.2627
Epoch 62/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 296ms/step - accuracy: 0.8977 - loss: 0.2950
Epoch 62: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 318ms/step - accuracy: 0.8979 - loss: 0.2945 - val_accuracy: 0.9298 - val_loss: 0.2655
Epoch 63/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 208ms/step - accuracy: 0.8995 - loss: 0.2855
Epoch 63: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 7s 226ms/step - accuracy: 0.8997 - loss: 0.2853 - val_accuracy: 0.9050 - val_loss: 0.3003
Epoch 64/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 208ms/step - accuracy: 0.8877 - loss: 0.3223
Epoch 64: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 226ms/step - accuracy: 0.8870 - loss: 0.3242 - val_accuracy: 0.9215 - val_loss: 0.2677
Epoch 65/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 289ms/step - accuracy: 0.8997 - loss: 0.3151
Epoch 65: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 308ms/step - accuracy: 0.8998 - loss: 0.3146 - val_accuracy: 0.9215 - val_loss: 0.2491
Epoch 66/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 240ms/step - accuracy: 0.9092 - loss: 0.2547
Epoch 66: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 274ms/step - accuracy: 0.9095 - loss: 0.2544 - val_accuracy: 0.9298 - val_loss: 0.2463
Epoch 67/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 211ms/step - accuracy: 0.9048 - loss: 0.2727
Epoch 67: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 229ms/step - accuracy: 0.9052 - loss: 0.2720 - val_accuracy: 0.9215 - val_loss: 0.2560
Epoch 68/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 318ms/step - accuracy: 0.9253 - loss: 0.2114
Epoch 68: val_accuracy did not improve from 0.92975
31/31 ━━━━━━━━━━━━━━━━━━━━ 10s 336ms/step - accuracy: 0.9253 - loss: 0.2118 - val_accuracy: 0.9174 - val_loss: 0.2578
Epoch 69/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 249ms/step - accuracy: 0.9372 - loss: 0.1952
Epoch 69: val_accuracy improved from 0.92975 to 0.93388, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 19s 269ms/step - accuracy: 0.9372 - loss: 0.1956 - val_accuracy: 0.9339 - val_loss: 0.2439
Epoch 70/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 316ms/step - accuracy: 0.9376 - loss: 0.2057
Epoch 70: val_accuracy did not improve from 0.93388
31/31 ━━━━━━━━━━━━━━━━━━━━ 12s 334ms/step - accuracy: 0.9373 - loss: 0.2058 - val_accuracy: 0.9174 - val_loss: 0.2475
Epoch 71/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 265ms/step - accuracy: 0.9373 - loss: 0.1763
Epoch 71: val_accuracy did not improve from 0.93388
31/31 ━━━━━━━━━━━━━━━━━━━━ 19s 284ms/step - accuracy: 0.9369 - loss: 0.1771 - val_accuracy: 0.9298 - val_loss: 0.2415
Epoch 72/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step - accuracy: 0.9313 - loss: 0.2223
Epoch 72: val_accuracy did not improve from 0.93388
31/31 ━━━━━━━━━━━━━━━━━━━━ 12s 340ms/step - accuracy: 0.9311 - loss: 0.2226 - val_accuracy: 0.9298 - val_loss: 0.2307
Epoch 73/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 291ms/step - accuracy: 0.9259 - loss: 0.2260
Epoch 73: val_accuracy did not improve from 0.93388
31/31 ━━━━━━━━━━━━━━━━━━━━ 20s 309ms/step - accuracy: 0.9261 - loss: 0.2256 - val_accuracy: 0.9298 - val_loss: 0.2620
Epoch 74/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 360ms/step - accuracy: 0.8880 - loss: 0.3490
Epoch 74: val_accuracy did not improve from 0.93388
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 419ms/step - accuracy: 0.8877 - loss: 0.3497 - val_accuracy: 0.9008 - val_loss: 0.2623
Epoch 75/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 325ms/step - accuracy: 0.9317 - loss: 0.2393
Epoch 75: val_accuracy did not improve from 0.93388
31/31 ━━━━━━━━━━━━━━━━━━━━ 18s 343ms/step - accuracy: 0.9316 - loss: 0.2389 - val_accuracy: 0.9298 - val_loss: 0.2650
Epoch 76/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 275ms/step - accuracy: 0.9249 - loss: 0.1885
Epoch 76: val_accuracy did not improve from 0.93388
31/31 ━━━━━━━━━━━━━━━━━━━━ 19s 294ms/step - accuracy: 0.9253 - loss: 0.1886 - val_accuracy: 0.9339 - val_loss: 0.2266
Epoch 77/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 287ms/step - accuracy: 0.9406 - loss: 0.1951
Epoch 77: val_accuracy did not improve from 0.93388
31/31 ━━━━━━━━━━━━━━━━━━━━ 11s 324ms/step - accuracy: 0.9404 - loss: 0.1959 - val_accuracy: 0.9256 - val_loss: 0.2314
Epoch 78/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 208ms/step - accuracy: 0.9421 - loss: 0.1933
Epoch 78: val_accuracy did not improve from 0.93388
31/31 ━━━━━━━━━━━━━━━━━━━━ 7s 227ms/step - accuracy: 0.9423 - loss: 0.1930 - val_accuracy: 0.9339 - val_loss: 0.2381
Epoch 79/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - accuracy: 0.9500 - loss: 0.1793
Epoch 79: val_accuracy improved from 0.93388 to 0.94215, saving model to best_model_lstm.keras
31/31 ━━━━━━━━━━━━━━━━━━━━ 13s 317ms/step - accuracy: 0.9500 - loss: 0.1788 - val_accuracy: 0.9421 - val_loss: 0.2435
Epoch 80/80
31/31 ━━━━━━━━━━━━━━━━━━━━ 0s 238ms/step - accuracy: 0.9541 - loss: 0.1510
Epoch 80: val_accuracy did not improve from 0.94215
31/31 ━━━━━━━━━━━━━━━━━━━━ 9s 271ms/step - accuracy: 0.9538 - loss: 0.1514 - val_accuracy: 0.9380 - val_loss: 0.2209


evaluate_model_performance(model, X_test, y_test)

8/8 ━━━━━━━━━━━━━━━━━━━━ 1s 124ms/step

              precision    recall  f1-score   support

           I       0.89      0.86      0.88        49
          II       0.90      0.88      0.89        49
         III       1.00      0.98      0.99        48
          IV       0.90      0.98      0.94        48
           V       1.00      1.00      1.00        48

    accuracy                           0.94       242
   macro avg       0.94      0.94      0.94       242
weighted avg       0.94      0.94      0.94       242


import pandas as pd

# Extract the 'Description' column as a Series
if isinstance(X_test_ini, pd.DataFrame) and 'Description' in X_test_ini.columns:
    X_test_ini = X_test_ini['Description'].values.ravel()  # Convert DataFrame to 1D array

# Ensure y_test_ini is a Series (already the case in your output)
# No need for any change here

# Create the DataFrame with cleaned and processed text
df_test_ini = pd.DataFrame({
    'Description': X_test_ini,
    'Accident Level': y_test_ini
})

# Apply the same NLP preprocessing pipeline to df_test_ini
df_test_ini['cleaned_text'] = df_test_ini['Description'].apply(remove_special_characters)
df_test_ini['cleaned_text'] = df_test_ini['cleaned_text'].str.lower()
df_test_ini['cleaned_text'] = df_test_ini['cleaned_text'].str.strip()
df_test_ini['cleaned_text'] = df_test_ini['cleaned_text'].replace(to_replace=r'\d', value='', regex=True)
df_test_ini['cleaned_text_without_stopwords'] = df_test_ini['cleaned_text'].apply(remove_stopwords)
df_test_ini['cleaned_text_without_stopwords'] = df_test_ini['cleaned_text_without_stopwords'].astype(str)
df_test_ini['cleaned_text_without_stopwords'] = df_test_ini['cleaned_text_without_stopwords'].fillna('')
df_test_ini['cleaned_text_stopwords_token'] = df_test_ini['cleaned_text_without_stopwords'].apply(lambda x: word_tokenize(x) if isinstance(x, str) else [])
df_test_ini['final_cleaned_text'] = df_test_ini['cleaned_text_stopwords_token'].apply(apply_lemmatizer)

# Create a list of tokenized words for the test set
words_list_test = [item.split(" ") for item in df_test_ini['final_cleaned_text'].values]

# Convert test text to sequences using the tokenizer
sequences_test = tokenizer.texts_to_sequences(words_list_test)

# Pad the sequences to ensure uniform length
X_test_padded = pad_sequences(sequences_test, maxlen=max_sentence_length)

# Extract the labels and apply one-hot encoding
y_test_encoded = encoder.transform(df_test_ini['Accident Level'].values.reshape(-1, 1))

print(f"X_test_padded shape: {X_test_padded.shape}, y_test_encoded shape: {y_test_encoded.shape}")

X_test_padded shape: (83, 109), y_test_encoded shape: (83, 5)


 model.evaluate(X_test_padded, y_test_encoded, verbose=1)

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 108ms/step - accuracy: 0.5488 - loss: 1.4640

[1.388720154762268, 0.5662650465965271]


evaluate_model_performance(model, X_test_padded, y_test_encoded)

3/3 ━━━━━━━━━━━━━━━━━━━━ 0s 88ms/step

              precision    recall  f1-score   support

           I       0.76      0.74      0.75        61
          II       0.07      0.12      0.09         8
         III       0.17      0.17      0.17         6
          IV       0.00      0.00      0.00         6
           V       0.00      0.00      0.00         2

    accuracy                           0.57        83
   macro avg       0.20      0.21      0.20        83
weighted avg       0.58      0.57      0.57        83


import pickle

# Pickle the model's architecture
with open('/content/drive/My Drive/AIML/best_BiLSTM_architecture.pkl', 'wb') as file:
    pickle.dump(model_Bi_LSTM.to_json(), file)

# Save the model's weights separately
model_Bi_LSTM.save_weights('/content/drive/My Drive/AIML/best_BiLSTM.weights.h5')

	Description	cleaned_text
0	While removing the drill rod of the Jumbo 08 for maintenance, the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal, seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this, at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo.	While removing the drill rod of the Jumbo 08 for maintenance the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo
1	During the activation of a sodium sulphide pump, the piping was uncoupled and the sulfide solution was designed in the area to reach the maid. Immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital. Note: of sulphide solution = 48 grams / liter.	During the activation of a sodium sulphide pump the piping was uncoupled and the sulfide solution was designed in the area to reach the maid Immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital Note of sulphide solution 48 grams liter
2	In the sub-station MILPO located at level +170 when the collaborator was doing the excavation work with a pick (hand tool), hitting a rock with the flat part of the beak, it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of the left foot of the collaborator causing the injury.	In the sub station MILPO located at level 170 when the collaborator was doing the excavation work with a pick hand tool hitting a rock with the flat part of the beak it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of the left foot of the collaborator causing the injury

	Description	cleaned_text
0	While removing the drill rod of the Jumbo 08 for maintenance, the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal, seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this, at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo.	while removing the drill rod of the jumbo 08 for maintenance the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo
1	During the activation of a sodium sulphide pump, the piping was uncoupled and the sulfide solution was designed in the area to reach the maid. Immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital. Note: of sulphide solution = 48 grams / liter.	during the activation of a sodium sulphide pump the piping was uncoupled and the sulfide solution was designed in the area to reach the maid immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital note of sulphide solution 48 grams liter
2	In the sub-station MILPO located at level +170 when the collaborator was doing the excavation work with a pick (hand tool), hitting a rock with the flat part of the beak, it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of the left foot of the collaborator causing the injury.	in the sub station milpo located at level 170 when the collaborator was doing the excavation work with a pick hand tool hitting a rock with the flat part of the beak it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of the left foot of the collaborator causing the injury

	Description	cleaned_text
0	While removing the drill rod of the Jumbo 08 for maintenance, the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal, seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this, at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo.	while removing the drill rod of the jumbo 08 for maintenance the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo
1	During the activation of a sodium sulphide pump, the piping was uncoupled and the sulfide solution was designed in the area to reach the maid. Immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital. Note: of sulphide solution = 48 grams / liter.	during the activation of a sodium sulphide pump the piping was uncoupled and the sulfide solution was designed in the area to reach the maid immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital note of sulphide solution 48 grams liter
2	In the sub-station MILPO located at level +170 when the collaborator was doing the excavation work with a pick (hand tool), hitting a rock with the flat part of the beak, it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of the left foot of the collaborator causing the injury.	in the sub station milpo located at level 170 when the collaborator was doing the excavation work with a pick hand tool hitting a rock with the flat part of the beak it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of the left foot of the collaborator causing the injury

	Description	cleaned_text
0	While removing the drill rod of the Jumbo 08 for maintenance, the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal, seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this, at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo.	while removing the drill rod of the jumbo for maintenance the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo
1	During the activation of a sodium sulphide pump, the piping was uncoupled and the sulfide solution was designed in the area to reach the maid. Immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital. Note: of sulphide solution = 48 grams / liter.	during the activation of a sodium sulphide pump the piping was uncoupled and the sulfide solution was designed in the area to reach the maid immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital note of sulphide solution grams liter
2	In the sub-station MILPO located at level +170 when the collaborator was doing the excavation work with a pick (hand tool), hitting a rock with the flat part of the beak, it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of the left foot of the collaborator causing the injury.	in the sub station milpo located at level when the collaborator was doing the excavation work with a pick hand tool hitting a rock with the flat part of the beak it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of the left foot of the collaborator causing the injury

	cleaned_text	cleaned_text_without_stopwords
0	while removing the drill rod of the jumbo for maintenance the supervisor proceeds to loosen the support of the intermediate centralizer to facilitate the removal seeing this the mechanic supports one end on the drill of the equipment to pull with both hands the bar and accelerate the removal from this at this moment the bar slides from its point of support and tightens the fingers of the mechanic between the drilling bar and the beam of the jumbo	removing drill rod jumbo maintenance supervisor proceeds loosen support intermediate centralizer facilitate removal seeing mechanic supports one end drill equipment pull hands bar accelerate removal moment bar slides point support tightens fingers mechanic drilling bar beam jumbo
1	during the activation of a sodium sulphide pump the piping was uncoupled and the sulfide solution was designed in the area to reach the maid immediately she made use of the emergency shower and was directed to the ambulatory doctor and later to the hospital note of sulphide solution grams liter	activation sodium sulphide pump piping uncoupled sulfide solution designed area reach maid immediately made use emergency shower directed ambulatory doctor later hospital note sulphide solution grams liter
2	in the sub station milpo located at level when the collaborator was doing the excavation work with a pick hand tool hitting a rock with the flat part of the beak it bounces off hitting the steel tip of the safety shoe and then the metatarsal area of the left foot of the collaborator causing the injury	sub station milpo located level collaborator excavation work pick hand tool hitting rock flat part beak bounces hitting steel tip safety shoe metatarsal area left foot collaborator causing injury

	Unnamed: 0	Data	Countries	Local	Industry Sector	Accident Level	Potential Accident Level	Genre	Employee or Third Party	Critical Risk	Description
0	0	2016-01-01	Country_01	Local_01	Mining	I	IV	Male	Third Party	Pressed	While removing the drill rod of the Jumbo 08 f...
1	1	2016-01-02	Country_02	Local_02	Mining	I	IV	Male	Employee	Pressurized Systems	During the activation of a sodium sulphide pum...
2	2	2016-01-06	Country_01	Local_03	Mining	I	III	Male	Third Party (Remote)	Manual Tools	In the sub-station MILPO located at level +170...
3	3	2016-01-08	Country_01	Local_04	Mining	I	I	Male	Third Party	Others	Being 9:45 am. approximately in the Nv. 1880 C...
4	4	2016-01-10	Country_01	Local_04	Mining	IV	IV	Male	Third Party	Others	Approximately at 11:45 a.m. in circumstances t...

	0
Date	0
Country	0
Local	0
Industry Sector	0
Accident Level	0
Potential Accident Level	0
Gender	0
EmployeeType	0
Critical Risk	0
Description	0

	cleaned_text_stopwords_token	cleaned_text_without_stopwords
0	[removing, drill, rod, jumbo, maintenance, supervisor, proceeds, loosen, support, intermediate, centralizer, facilitate, removal, seeing, mechanic, supports, one, end, drill, equipment, pull, hands, bar, accelerate, removal, moment, bar, slides, point, support, tightens, fingers, mechanic, drilling, bar, beam, jumbo]	removing drill rod jumbo maintenance supervisor proceeds loosen support intermediate centralizer facilitate removal seeing mechanic supports one end drill equipment pull hands bar accelerate removal moment bar slides point support tightens fingers mechanic drilling bar beam jumbo
1	[activation, sodium, sulphide, pump, piping, uncoupled, sulfide, solution, designed, area, reach, maid, immediately, made, use, emergency, shower, directed, ambulatory, doctor, later, hospital, note, sulphide, solution, grams, liter]	activation sodium sulphide pump piping uncoupled sulfide solution designed area reach maid immediately made use emergency shower directed ambulatory doctor later hospital note sulphide solution grams liter
2	[sub, station, milpo, located, level, collaborator, excavation, work, pick, hand, tool, hitting, rock, flat, part, beak, bounces, hitting, steel, tip, safety, shoe, metatarsal, area, left, foot, collaborator, causing, injury]	sub station milpo located level collaborator excavation work pick hand tool hitting rock flat part beak bounces hitting steel tip safety shoe metatarsal area left foot collaborator causing injury

	approximately	area	assistant	auxiliary	bar	beam	begin	bolt	bounce	causing	circumstance	climb	cm	collaborator	come	coming	company	cx	described	directed	direction	distance	doctor	drill	drilling	emergency	end	equipment	fall	finger	foot	hand	head	high	hit	hitting	hospital	identified	immediately	impromec	injured	injury	instep	intermediate	jumbo	key	kg	later	leaving	left	length	level	lever	located	locked	loosen	machine	made	maid	maintenance	mechanic	metatarsal	meter	moment	motor	mr	note	nv	ob	one	palm	part	performed	personnel	pick	platform	point	pressure	prevent	proceed	proceeding	proceeds	pull	pulley	pump	reach	removal	removing	right	rock	rod	safety	shoe	slide	solution	station	steel	supervisor	support	task	three	tip	tool	two	unlocking	use	weight	work	worker
0	0	0	0	0	3	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	1	0	1	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	2	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	2	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0	0	2	1	0	0	1	0	0	1	0	0	0	1	3	0	0	0	0	0	0	0	0	0	0
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	0	0	0
2	0	1	0	0	0	0	0	0	1	1	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	2	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	1	1	0	0	0	0	1	1	0	0	0	0	1	0
3	1	0	1	1	0	0	1	4	0	1	0	1	0	1	0	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	2	1	0	0	1	0	1	0	0	0	1	0	0	0	2	0	0	1	1	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	1	0	1	0	0	1	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0
4	1	0	0	0	0	0	0	0	0	1	1	0	2	0	1	0	1	0	1	0	0	1	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	1	0	0	0	1	1	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	2	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1

	Unnamed: 0	Accident Level	Potential Accident Level	Month	WeekDay	Season	Local_02	Local_03	Local_04	Local_05	Local_06	Local_07	Local_08	Local_09	Local_10	Local_11	Local_12	Male	IS_Mining	IS_Others	EmpType_Third Party	EmpType_Third Party (Remote)	CR_Bees	CR_Blocking and isolation of energies	CR_Burn	CR_Chemical substances	CR_Confined space	CR_Cut	CR_Electrical Shock	CR_Electrical installation	CR_Fall	CR_Fall prevention	CR_Fall prevention (same level)	CR_Individual protection equipment	CR_Liquid Metal	CR_Machine Protection	CR_Manual Tools	CR_Others	CR_Plates	CR_Poll	CR_Power lock	CR_Pressed	CR_Pressurized Systems	CR_Pressurized Systems / Chemical Substances	CR_Projection	CR_Projection of fragments	CR_Projection/Burning	CR_Projection/Choco	CR_Projection/Manual Tools	CR_Suspended Loads	CR_Traffic	CR_Vehicles and Mobile Equipment	CR_Venomous Animals	CR_remains of choco	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	Feature 10	Feature 11	Feature 12	Feature 13	Feature 14	Feature 15	Feature 16	Feature 17	Feature 18	Feature 19	Feature 20	Feature 21	Feature 22	Feature 23	Feature 24	Feature 25	Feature 26	Feature 27	Feature 28	Feature 29	Feature 30	Feature 31	Feature 32	Feature 33	Feature 34	Feature 35	Feature 36	Feature 37	Feature 38	Feature 39	Feature 40	Feature 41	Feature 42	Feature 43	Feature 44	Feature 45	Feature 46	Feature 47	Feature 48	Feature 49	Feature 50	Feature 51	Feature 52	Feature 53	Feature 54	Feature 55	Feature 56	Feature 57	Feature 58	Feature 59	Feature 60	Feature 61	Feature 62	Feature 63	Feature 64	Feature 65	Feature 66	Feature 67	Feature 68	Feature 69	Feature 70	Feature 71	Feature 72	Feature 73	Feature 74	Feature 75	Feature 76	Feature 77	Feature 78	Feature 79	Feature 80	Feature 81	Feature 82	Feature 83	Feature 84	Feature 85	Feature 86	Feature 87	Feature 88	Feature 89	Feature 90	Feature 91	Feature 92	Feature 93	Feature 94	Feature 95	Feature 96	Feature 97	Feature 98	Feature 99
0	0	0	3	1	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	-0.010853	0.020547	0.005375	-0.002241	0.001997	-0.028157	0.006820	0.036147	-0.005286	-0.013776	-0.014756	-0.027444	-0.005455	0.002394	0.002100	-0.012731	-0.000240	-0.029227	-0.002906	-0.031793	0.011095	0.009675	0.002929	-0.005923	-0.007694	0.001864	-0.019409	-0.014207	-0.019993	0.000057	0.016635	0.003181	0.001025	-0.013923	-0.006169	0.012964	0.006393	-0.016192	-0.009012	-0.031610	0.001852	-0.016327	-0.004483	0.002451	0.016484	-0.007774	-0.018939	0.002269	0.007388	0.017157	0.006435	-0.017414	-0.008086	-0.009292	-0.012344	0.011447	0.005183	-0.005222	-0.020776	0.000791	0.006416	0.003569	-0.006255	0.002391	-0.020580	0.017828	0.002866	0.014274	-0.020424	0.026225	-0.016350	0.012527	0.024639	-0.006678	0.019729	0.008666	-0.000045	-0.005449	-0.014707	0.006593	-0.011309	-0.004738	-0.018564	0.030252	0.000353	0.003804	-0.002595	0.024898	0.022078	0.003241	0.018453	0.010212	0.004373	0.005605	0.028741	0.016709	0.008705	-0.012103	0.006409	-0.006034
1	1	0	3	1	2	2	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	-0.004616	0.009492	0.000739	-0.000792	-0.000834	-0.010280	0.001925	0.015885	0.001348	-0.005405	-0.005171	-0.011610	-0.002235	0.001352	0.000452	-0.004559	0.002219	-0.010917	-0.003119	-0.012339	0.006182	0.003048	0.000517	-0.000877	-0.003482	0.000920	-0.006669	-0.005157	-0.008463	-0.000597	0.005898	0.000892	0.000127	-0.005487	-0.003291	0.008170	0.001339	-0.007797	-0.004052	-0.013361	-0.001077	-0.005474	-0.001497	0.000410	0.004890	-0.005149	-0.007524	0.001791	0.001989	0.004215	0.002689	-0.009527	-0.002728	-0.003291	-0.004751	0.004388	0.001506	-0.001612	-0.007822	-0.000642	0.001114	0.002370	-0.000255	-0.000210	-0.009445	0.007675	0.001495	0.006687	-0.009303	0.010048	-0.006069	0.004986	0.010948	-0.003086	0.008785	0.002961	-0.001677	-0.002906	-0.007609	0.001743	-0.002600	-0.001839	-0.008553	0.012533	-0.000946	0.000685	-0.001735	0.009657	0.008161	0.000624	0.008117	0.004937	0.000916	0.002784	0.014321	0.008871	0.003808	-0.005125	0.003032	-0.001531

	Accident Level	Potential Accident Level	Month	WeekDay	Season	Local_02	Local_03	Local_04	Local_05	Local_06	Local_07	Local_08	Local_09	Local_10	Local_11	Local_12	Male	IS_Mining	IS_Others	EmpType_Third Party	EmpType_Third Party (Remote)	CR_Bees	CR_Blocking and isolation of energies	CR_Burn	CR_Chemical substances	CR_Confined space	CR_Cut	CR_Electrical Shock	CR_Electrical installation	CR_Fall	CR_Fall prevention	CR_Fall prevention (same level)	CR_Individual protection equipment	CR_Liquid Metal	CR_Machine Protection	CR_Manual Tools	CR_Others	CR_Plates	CR_Poll	CR_Power lock	CR_Pressed	CR_Pressurized Systems	CR_Pressurized Systems / Chemical Substances	CR_Projection	CR_Projection of fragments	CR_Projection/Burning	CR_Projection/Choco	CR_Projection/Manual Tools	CR_Suspended Loads	CR_Traffic	CR_Vehicles and Mobile Equipment	CR_Venomous Animals	CR_remains of choco	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	Feature 10	Feature 11	Feature 12	Feature 13	Feature 14	Feature 15	Feature 16	Feature 17	Feature 18	Feature 19	Feature 20	Feature 21	Feature 22	Feature 23	Feature 24	Feature 25	Feature 26	Feature 27	Feature 28	Feature 29	Feature 30	Feature 31	Feature 32	Feature 33	Feature 34	Feature 35	Feature 36	Feature 37	Feature 38	Feature 39	Feature 40	Feature 41	Feature 42	Feature 43	Feature 44	Feature 45	Feature 46	Feature 47	Feature 48	Feature 49	Feature 50	Feature 51	Feature 52	Feature 53	Feature 54	Feature 55	Feature 56	Feature 57	Feature 58	Feature 59	Feature 60	Feature 61	Feature 62	Feature 63	Feature 64	Feature 65	Feature 66	Feature 67	Feature 68	Feature 69	Feature 70	Feature 71	Feature 72	Feature 73	Feature 74	Feature 75	Feature 76	Feature 77	Feature 78	Feature 79	Feature 80	Feature 81	Feature 82	Feature 83	Feature 84	Feature 85	Feature 86	Feature 87	Feature 88	Feature 89	Feature 90	Feature 91	Feature 92	Feature 93	Feature 94	Feature 95	Feature 96	Feature 97	Feature 98	Feature 99
0	0	3	1	0	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	-0.011391	0.019539	0.005037	-0.001186	0.000264	-0.027592	0.006185	0.035908	-0.006610	-0.012498	-0.016226	-0.026945	-0.004458	0.003966	0.002733	-0.014317	0.000835	-0.028687	-0.002087	-0.032298	0.012612	0.008773	0.003588	-0.005270	-0.006541	0.002910	-0.020650	-0.014154	-0.019608	-0.000338	0.017284	0.003972	0.000696	-0.014957	-0.00536	0.013549	0.005534	-0.016424	-0.008909	-0.032758	0.000412	-0.016006	-0.005172	0.001264	0.016677	-0.006267	-0.017369	0.002550	0.007144	0.017377	0.008351	-0.016884	-0.007985	-0.007540	-0.012374	0.012227	0.005867	-0.005535	-0.020720	0.000629	0.004614	0.003622	-0.006843	0.002838	-0.021073	0.017466	0.002683	0.014828	-0.021249	0.026359	-0.016745	0.011570	0.025020	-0.006876	0.019745	0.009426	0.000155	-0.006066	-0.013756	0.007337	-0.011324	-0.004601	-0.017766	0.029461	-0.001988	0.001783	-0.001889	0.024445	0.022100	0.003630	0.017229	0.010601	0.004675	0.004973	0.028542	0.015719	0.007889	-0.012283	0.005376	-0.005002
1	0	3	1	2	2	1	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	-0.004790	0.009005	0.000574	-0.000356	-0.001509	-0.009943	0.001616	0.015639	0.000862	-0.004875	-0.005683	-0.011285	-0.001818	0.001970	0.000691	-0.005156	0.002635	-0.010610	-0.002738	-0.012396	0.006723	0.002641	0.000777	-0.000599	-0.002999	0.001351	-0.007058	-0.005087	-0.008222	-0.000749	0.006081	0.001198	0.000015	-0.005806	-0.00292	0.008321	0.000971	-0.007775	-0.003964	-0.013689	-0.001629	-0.005285	-0.001732	-0.000070	0.004864	-0.004508	-0.006811	0.001868	0.001853	0.004220	0.003428	-0.009220	-0.002631	-0.002542	-0.004689	0.004665	0.001758	-0.001720	-0.007648	-0.000722	0.000363	0.002396	-0.000487	-0.000081	-0.009561	0.007441	0.001414	0.006853	-0.009519	0.009970	-0.006136	0.004581	0.011008	-0.003112	0.008724	0.003226	-0.001584	-0.003123	-0.007171	0.001980	-0.002539	-0.001732	-0.008126	0.012065	-0.001839	-0.000115	-0.001438	0.009345	0.008068	0.000776	0.007566	0.005012	0.001035	0.002499	0.014110	0.008417	0.003482	-0.005128	0.002610	-0.001089

	Description Of Data	ModelName	Train Accuracy	Test Accuracy	Precision	Recall	F1
0	Word2Vec-Basic-Accident	KNN	0.997006	0.690476	0.555968	0.690476	0.615964
1	Word2Vec-Basic-Accident	Ridge	0.763473	0.738095	0.544785	0.738095	0.626875
2	Word2Vec-Basic-Accident	LogisticRegression	0.742515	0.726190	0.542456	0.726190	0.621018
3	Word2Vec-Basic-Accident	GradientBoost	0.997006	0.726190	0.549071	0.726190	0.625331
4	Word2Vec-Basic-Accident	SVM	0.739521	0.738095	0.544785	0.738095	0.626875
5	Word2Vec-Basic-Accident	Bagging	0.967066	0.726190	0.549071	0.726190	0.625331
6	Word2Vec-Basic-Accident	RandomForest	0.997006	0.738095	0.551348	0.738095	0.631199
7	Word2Vec-Basic-Accident	DecisionTree	0.799401	0.642857	0.559631	0.642857	0.598281
8	Word2Vec-Basic-Accident	AdaBoost	0.733533	0.726190	0.549071	0.726190	0.625331
9	Word2Vec-Basic-Accident	XGBoost	0.997006	0.714286	0.546737	0.714286	0.619381
10	Word2Vec-Basic-Accident	Gradient Boost	0.925150	0.726190	0.549071	0.726190	0.625331
11	Glove-Basic-Accident	KNN	0.997006	0.726190	0.589286	0.726190	0.641600
12	Glove-Basic-Accident	Ridge	0.826347	0.702381	0.613321	0.702381	0.627635
13	Glove-Basic-Accident	LogisticRegression	0.790419	0.726190	0.618166	0.726190	0.639789
14	Glove-Basic-Accident	GradientBoost	0.997006	0.738095	0.672696	0.738095	0.680156
15	Glove-Basic-Accident	SVM	0.739521	0.738095	0.544785	0.738095	0.626875
16	Glove-Basic-Accident	Bagging	0.949102	0.726190	0.660792	0.726190	0.662866
17	Glove-Basic-Accident	RandomForest	0.997006	0.738095	0.551348	0.738095	0.631199
18	Glove-Basic-Accident	DecisionTree	0.856287	0.607143	0.566527	0.607143	0.583639
19	Glove-Basic-Accident	AdaBoost	0.736527	0.726190	0.542456	0.726190	0.621018
20	Glove-Basic-Accident	XGBoost	0.997006	0.726190	0.555850	0.726190	0.629704
21	Glove-Basic-Accident	Gradient Boost	0.958084	0.726190	0.639194	0.726190	0.653061
22	TfIdf-Basic-Accident	KNN	0.997006	0.726190	0.625000	0.726190	0.644151
23	TfIdf-Basic-Accident	Ridge	0.979042	0.738095	0.558072	0.738095	0.635582
24	TfIdf-Basic-Accident	LogisticRegression	0.793413	0.738095	0.551348	0.738095	0.631199
25	TfIdf-Basic-Accident	GradientBoost	0.997006	0.714286	0.560579	0.714286	0.628166
26	TfIdf-Basic-Accident	SVM	0.739521	0.738095	0.544785	0.738095	0.626875
27	TfIdf-Basic-Accident	Bagging	0.949102	0.702381	0.551236	0.702381	0.617697
28	TfIdf-Basic-Accident	RandomForest	0.967066	0.702381	0.575015	0.702381	0.630616
29	TfIdf-Basic-Accident	DecisionTree	0.778443	0.654762	0.548584	0.654762	0.596989
30	TfIdf-Basic-Accident	AdaBoost	0.760479	0.726190	0.542456	0.726190	0.621018
31	TfIdf-Basic-Accident	XGBoost	0.997006	0.690476	0.579115	0.690476	0.629902
32	TfIdf-Basic-Accident	Gradient Boost	0.892216	0.726190	0.639194	0.726190	0.653061
33	Word2Vec-PCA&Upsampled-Accident	KNN	0.999190	0.380952	0.646142	0.380952	0.457177
34	Word2Vec-PCA&Upsampled-Accident	Ridge	0.342510	0.214286	0.701326	0.214286	0.277041
35	Word2Vec-PCA&Upsampled-Accident	LogisticRegression	0.351417	0.226190	0.628743	0.226190	0.297494
36	Word2Vec-PCA&Upsampled-Accident	GradientBoost	0.995142	0.607143	0.595238	0.607143	0.600000
37	Word2Vec-PCA&Upsampled-Accident	SVM	0.731984	0.333333	0.671006	0.333333	0.384510
38	Word2Vec-PCA&Upsampled-Accident	Bagging	0.997571	0.666667	0.597959	0.666667	0.629870
39	Word2Vec-PCA&Upsampled-Accident	RandomForest	0.973279	0.571429	0.615832	0.571429	0.589235
40	Word2Vec-PCA&Upsampled-Accident	DecisionTree	0.731984	0.261905	0.557965	0.261905	0.308803
41	Word2Vec-PCA&Upsampled-Accident	AdaBoost	0.368421	0.178571	0.642328	0.178571	0.204992
42	Word2Vec-PCA&Upsampled-Accident	XGBoost	0.999190	0.654762	0.612045	0.654762	0.630952
43	Word2Vec-PCA&Upsampled-Accident	Gradient Boost	0.907692	0.392857	0.552976	0.392857	0.445675
44	Word2Vec-PCA&SMOTE-Accident	KNN	0.999190	0.380952	0.646142	0.380952	0.457177
45	Word2Vec-PCA&SMOTE-Accident	Ridge	0.342510	0.214286	0.701326	0.214286	0.277041
46	Word2Vec-PCA&SMOTE-Accident	LogisticRegression	0.351417	0.226190	0.628743	0.226190	0.297494
47	Word2Vec-PCA&SMOTE-Accident	GradientBoost	0.995142	0.607143	0.595238	0.607143	0.600000
48	Word2Vec-PCA&SMOTE-Accident	SVM	0.731984	0.333333	0.671006	0.333333	0.384510
49	Word2Vec-PCA&SMOTE-Accident	Bagging	0.996761	0.642857	0.605339	0.642857	0.623512
50	Word2Vec-PCA&SMOTE-Accident	RandomForest	0.973279	0.571429	0.615832	0.571429	0.589235
51	Word2Vec-PCA&SMOTE-Accident	DecisionTree	0.731984	0.261905	0.557965	0.261905	0.308803
52	Word2Vec-PCA&SMOTE-Accident	AdaBoost	0.368421	0.178571	0.642328	0.178571	0.204992
53	Word2Vec-PCA&SMOTE-Accident	XGBoost	0.999190	0.654762	0.612045	0.654762	0.630952
54	Word2Vec-PCA&SMOTE-Accident	Gradient Boost	0.907692	0.392857	0.552976	0.392857	0.445675
55	Glove-PCA&Upsampled-Accident	KNN	0.999190	0.345238	0.676337	0.345238	0.405970
56	Glove-PCA&Upsampled-Accident	Ridge	0.504453	0.261905	0.647797	0.261905	0.322394
57	Glove-PCA&Upsampled-Accident	LogisticRegression	0.525506	0.285714	0.689197	0.285714	0.327978
58	Glove-PCA&Upsampled-Accident	GradientBoost	0.999190	0.678571	0.546382	0.678571	0.605344
59	Glove-PCA&Upsampled-Accident	SVM	0.841296	0.392857	0.594132	0.392857	0.439032
60	Glove-PCA&Upsampled-Accident	Bagging	0.999190	0.654762	0.556570	0.654762	0.601681
61	Glove-PCA&Upsampled-Accident	RandomForest	0.995142	0.642857	0.545988	0.642857	0.590476
62	Glove-PCA&Upsampled-Accident	DecisionTree	0.883401	0.333333	0.610851	0.333333	0.403970
63	Glove-PCA&Upsampled-Accident	AdaBoost	0.298785	0.214286	0.563821	0.214286	0.285820
64	Glove-PCA&Upsampled-Accident	XGBoost	0.999190	0.666667	0.543860	0.666667	0.599034
65	Glove-PCA&Upsampled-Accident	Gradient Boost	0.976518	0.488095	0.526620	0.488095	0.506224
66	Glove-PCA&SMOTE-Accident	KNN	0.999190	0.345238	0.676337	0.345238	0.405970
67	Glove-PCA&SMOTE-Accident	Ridge	0.504453	0.261905	0.647797	0.261905	0.322394
68	Glove-PCA&SMOTE-Accident	LogisticRegression	0.525506	0.285714	0.689197	0.285714	0.327978
69	Glove-PCA&SMOTE-Accident	GradientBoost	0.999190	0.678571	0.546382	0.678571	0.605344
70	Glove-PCA&SMOTE-Accident	SVM	0.841296	0.392857	0.594132	0.392857	0.439032
71	Glove-PCA&SMOTE-Accident	Bagging	0.999190	0.654762	0.562420	0.654762	0.602007
72	Glove-PCA&SMOTE-Accident	RandomForest	0.995142	0.642857	0.545988	0.642857	0.590476
73	Glove-PCA&SMOTE-Accident	DecisionTree	0.883401	0.333333	0.610851	0.333333	0.403970
74	Glove-PCA&SMOTE-Accident	AdaBoost	0.298785	0.214286	0.563821	0.214286	0.285820
75	Glove-PCA&SMOTE-Accident	XGBoost	0.999190	0.666667	0.543860	0.666667	0.599034
76	Glove-PCA&SMOTE-Accident	Gradient Boost	0.975709	0.488095	0.526620	0.488095	0.506224
77	TFIDF-PCA&Upsampled-Accident	KNN	0.999190	0.369048	0.640616	0.369048	0.427235
78	TFIDF-PCA&Upsampled-Accident	Ridge	0.527935	0.321429	0.629473	0.321429	0.395280
79	TFIDF-PCA&Upsampled-Accident	LogisticRegression	0.579757	0.357143	0.669513	0.357143	0.427688
80	TFIDF-PCA&Upsampled-Accident	GradientBoost	0.999190	0.654762	0.606463	0.654762	0.624459
81	TFIDF-PCA&Upsampled-Accident	SVM	0.846154	0.380952	0.565176	0.380952	0.438213
82	TFIDF-PCA&Upsampled-Accident	Bagging	0.999190	0.619048	0.612554	0.619048	0.615288
83	TFIDF-PCA&Upsampled-Accident	RandomForest	0.982186	0.619048	0.625649	0.619048	0.621255
84	TFIDF-PCA&Upsampled-Accident	DecisionTree	0.847773	0.369048	0.603228	0.369048	0.426846
85	TFIDF-PCA&Upsampled-Accident	AdaBoost	0.275304	0.166667	0.807593	0.166667	0.124147
86	TFIDF-PCA&Upsampled-Accident	XGBoost	0.999190	0.678571	0.612245	0.678571	0.642857
87	TFIDF-PCA&Upsampled-Accident	Gradient Boost	0.962753	0.607143	0.647851	0.607143	0.624017
88	TFIDF-PCA&SMOTE-Accident	KNN	0.999190	0.369048	0.640616	0.369048	0.427235
89	TFIDF-PCA&SMOTE-Accident	Ridge	0.527935	0.321429	0.629473	0.321429	0.395280
90	TFIDF-PCA&SMOTE-Accident	LogisticRegression	0.579757	0.357143	0.669513	0.357143	0.427688
91	TFIDF-PCA&SMOTE-Accident	GradientBoost	0.999190	0.654762	0.606463	0.654762	0.624459
92	TFIDF-PCA&SMOTE-Accident	SVM	0.846154	0.380952	0.565176	0.380952	0.438213
93	TFIDF-PCA&SMOTE-Accident	Bagging	0.996761	0.583333	0.584569	0.583333	0.583120
94	TFIDF-PCA&SMOTE-Accident	RandomForest	0.982186	0.619048	0.625649	0.619048	0.621255
95	TFIDF-PCA&SMOTE-Accident	DecisionTree	0.847773	0.369048	0.603228	0.369048	0.426846
96	TFIDF-PCA&SMOTE-Accident	AdaBoost	0.275304	0.166667	0.807593	0.166667	0.124147
97	TFIDF-PCA&SMOTE-Accident	XGBoost	0.999190	0.678571	0.612245	0.678571	0.642857
98	TFIDF-PCA&SMOTE-Accident	Gradient Boost	0.962753	0.607143	0.647851	0.607143	0.624017

	Description Of Data	ModelName	Train Accuracy	Test Accuracy	Precision	Recall	F1
0	Potential Acc: Word2Vec-Basic-Accident	KNN	1.000000	0.333333	0.351785	0.333333	0.323133
1	Potential Acc: Word2Vec-Basic-Accident	Ridge	0.580838	0.345238	0.318958	0.345238	0.314690
2	Potential Acc: Word2Vec-Basic-Accident	LogisticRegression	0.574850	0.345238	0.331143	0.345238	0.319466
3	Potential Acc: Word2Vec-Basic-Accident	GradientBoost	1.000000	0.428571	0.454034	0.428571	0.420352
4	Potential Acc: Word2Vec-Basic-Accident	SVM	0.479042	0.333333	0.270002	0.333333	0.238974
5	Potential Acc: Word2Vec-Basic-Accident	Bagging	0.985030	0.357143	0.355263	0.357143	0.350662
6	Potential Acc: Word2Vec-Basic-Accident	RandomForest	1.000000	0.380952	0.395105	0.380952	0.354048
7	Potential Acc: Word2Vec-Basic-Accident	DecisionTree	0.670659	0.392857	0.425240	0.392857	0.391922
8	Potential Acc: Word2Vec-Basic-Accident	AdaBoost	0.395210	0.333333	0.252778	0.333333	0.260916
9	Potential Acc: Word2Vec-Basic-Accident	XGBoost	1.000000	0.369048	0.399282	0.369048	0.355182
10	Potential Acc: Word2Vec-Basic-Accident	Gradient Boost	0.967066	0.416667	0.446897	0.416667	0.391602
11	Potential Acc: Glove-Basic-Accident	KNN	1.000000	0.309524	0.342560	0.309524	0.299440
12	Potential Acc: Glove-Basic-Accident	Ridge	0.769461	0.392857	0.395048	0.392857	0.382516
13	Potential Acc: Glove-Basic-Accident	LogisticRegression	0.685629	0.333333	0.349567	0.333333	0.332524
14	Potential Acc: Glove-Basic-Accident	GradientBoost	1.000000	0.488095	0.532038	0.488095	0.472378
15	Potential Acc: Glove-Basic-Accident	SVM	0.491018	0.345238	0.306831	0.345238	0.258694
16	Potential Acc: Glove-Basic-Accident	Bagging	0.982036	0.333333	0.333013	0.333333	0.321007
17	Potential Acc: Glove-Basic-Accident	RandomForest	1.000000	0.369048	0.333354	0.369048	0.318751
18	Potential Acc: Glove-Basic-Accident	DecisionTree	0.670659	0.380952	0.351380	0.380952	0.358427
19	Potential Acc: Glove-Basic-Accident	AdaBoost	0.359281	0.309524	0.178995	0.309524	0.198791
20	Potential Acc: Glove-Basic-Accident	XGBoost	1.000000	0.369048	0.377111	0.369048	0.347764
21	Potential Acc: Glove-Basic-Accident	Gradient Boost	0.970060	0.392857	0.398864	0.392857	0.369553
22	Potential Acc: TFIDF-Basic-Accident	KNN	1.000000	0.357143	0.382876	0.357143	0.343491
23	Potential Acc: TFIDF-Basic-Accident	Ridge	1.000000	0.416667	0.439342	0.416667	0.396545
24	Potential Acc: TFIDF-Basic-Accident	LogisticRegression	0.829341	0.428571	0.429549	0.428571	0.409535
25	Potential Acc: TFIDF-Basic-Accident	GradientBoost	1.000000	0.452381	0.476030	0.452381	0.424106
26	Potential Acc: TFIDF-Basic-Accident	SVM	0.494012	0.333333	0.268507	0.333333	0.237416
27	Potential Acc: TFIDF-Basic-Accident	Bagging	0.985030	0.357143	0.354722	0.357143	0.350317
28	Potential Acc: TFIDF-Basic-Accident	RandomForest	0.865269	0.369048	0.360937	0.369048	0.349767
29	Potential Acc: TFIDF-Basic-Accident	DecisionTree	0.562874	0.321429	0.280764	0.321429	0.277755
30	Potential Acc: TFIDF-Basic-Accident	AdaBoost	0.440120	0.369048	0.402087	0.369048	0.339074
31	Potential Acc: TFIDF-Basic-Accident	XGBoost	1.000000	0.440476	0.459456	0.440476	0.433294
32	Potential Acc: TFIDF-Basic-Accident	Gradient Boost	0.937126	0.464286	0.483662	0.464286	0.419232

	Data Description	Model Description	Train Accuracy	Test Accuracy	F1 Score
0	W2V Basic	Basic Model with 1 layer	78.44	75.00	65.16
1	Glove Basic Data	Basic Model with 1 layer	91.02	73.81	69.07
2	TFIDF Basic Data	Basic Model with 1 layer	94.01	70.24	61.33
3	W2V Basic Data	NN Classifier Layers-Dropouts-Normalization-SGD	73.95	73.81	62.69
4	Glove Basic data	NN Classifier Layers-Dropouts-Normalization-SGD	73.95	73.81	62.69
5	TFIDF Basic data	NN Classifier Layers-Dropouts-Normalization-SGD	73.95	73.81	62.69
6	W2V Basic Data	NN Classifier Layers-Dropouts-Normalization-ADAM	73.95	71.43	61.94
7	Glove Basic Data	NN Classifier Layers-Dropouts-Normalization-ADAM	74.25	73.81	62.69
8	TFIDF Basic Data	NN Classifier Layers-Dropouts-Normalization-ADAM	74.55	73.81	62.69
9	W2V Basic	Basic w2v NN model with best parameters	73.95	73.81	62.69
10	TFIDF Basic	NN Classifier Model Basic TFIDF with best parameters	73.95	73.81	62.69
11	W2V Basic	RNN Classifier Basic W2V model	73.95	73.81	62.69
12	TFIDF Basic	RNN Classifier Basic TFIDF model	73.95	73.81	62.69
13	RNN-W2V Basic-with best params	SimpleRNN	73.95	73.81	62.69
14	RNN on TfIdf Basic with best params	SimpleRNN	73.95	73.81	62.69
15	LSTM on W2V	LSTM on W2V dataset	74.41	74.12	63.10

	Data Description	Model Description	Train Accuracy	Test Accuracy	F1 Score
0	LSTM on W2V using augmented data	LSTM on W2V-augmented dataset	94.09	91.69	90.20
1	BiDirectional LSTM on W2V using augmented data	LSTM on W2V dataset	94.76	90.03	90.82

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	Feature 10	Feature 11	Feature 12	Feature 13	Feature 14	Feature 15	Feature 16	Feature 17	Feature 18	Feature 19	Feature 20	Feature 21	Feature 22	Feature 23	Feature 24	Feature 25	Feature 26	Feature 27	Feature 28	Feature 29	Feature 30	Feature 31	Feature 32	Feature 33	Feature 34	Feature 35	Feature 36	Feature 37	Feature 38	Feature 39	Feature 40	Feature 41	Feature 42	Feature 43	Feature 44	Feature 45	Feature 46	Feature 47	Feature 48	Feature 49	Feature 50	Feature 51	Feature 52	Feature 53	Feature 54	Feature 55	Feature 56	Feature 57	Feature 58	Feature 59	Feature 60	Feature 61	Feature 62	Feature 63	Feature 64	Feature 65	Feature 66	Feature 67	Feature 68	Feature 69	Feature 70	Feature 71	Feature 72	Feature 73	Feature 74	Feature 75	Feature 76	Feature 77	Feature 78	Feature 79	Feature 80	Feature 81	Feature 82	Feature 83	Feature 84	Feature 85	Feature 86	Feature 87	Feature 88	Feature 89	Feature 90	Feature 91	Feature 92	Feature 93	Feature 94	Feature 95	Feature 96	Feature 97	Feature 98	Feature 99
0	-0.010853	0.020547	0.005375	-2.240915e-03	0.001997	-0.028157	0.006820	0.036147	-0.005286	-0.013776	-0.014756	-0.027444	-0.005455	0.002394	0.002100	-0.012731	-2.398028e-04	-0.029227	-0.002906	-0.031793	0.011095	0.009675	0.002929	-0.005923	-0.007694	0.001864	-0.019409	-0.014207	-0.019993	0.000057	0.016635	0.003181	1.024854e-03	-0.013923	-0.006169	0.012964	0.006393	-0.016192	-0.009012	-0.031610	0.001852	-0.016327	-0.004483	0.002451	0.016484	-0.007774	-0.018939	2.269392e-03	0.007388	0.017157	0.006435	-0.017414	-0.008086	-0.009292	-0.012344	0.011447	0.005183	-0.005222	-0.020776	7.913968e-04	0.006416	0.003569	-0.006255	0.002391	-0.020580	0.017828	0.002866	0.014274	-0.020424	0.026225	-0.016350	0.012527	0.024639	-0.006678	0.019729	0.008666	-0.000045	-0.005449	-0.014707	0.006593	-0.011309	-0.004738	-0.018564	0.030252	0.000353	0.003804	-0.002595	0.024898	0.022078	0.003241	0.018453	0.010212	0.004373	0.005605	0.028741	0.016709	0.008705	-0.012103	0.006409	-0.006034
1	-0.004616	0.009492	0.000739	-7.922249e-04	-0.000834	-0.010280	0.001925	0.015885	0.001348	-0.005405	-0.005171	-0.011610	-0.002235	0.001352	0.000452	-0.004559	2.218579e-03	-0.010917	-0.003119	-0.012339	0.006182	0.003048	0.000517	-0.000877	-0.003482	0.000920	-0.006669	-0.005157	-0.008463	-0.000597	0.005898	0.000892	1.273722e-04	-0.005487	-0.003291	0.008170	0.001339	-0.007797	-0.004052	-0.013361	-0.001077	-0.005474	-0.001497	0.000410	0.004890	-0.005149	-0.007524	1.790682e-03	0.001989	0.004215	0.002689	-0.009527	-0.002728	-0.003291	-0.004751	0.004388	0.001506	-0.001612	-0.007822	-6.422810e-04	0.001114	0.002370	-0.000255	-0.000210	-0.009445	0.007675	0.001495	0.006687	-0.009303	0.010048	-0.006069	0.004986	0.010948	-0.003086	0.008785	0.002961	-0.001677	-0.002906	-0.007609	0.001743	-0.002600	-0.001839	-0.008553	0.012533	-0.000946	0.000685	-0.001735	0.009657	0.008161	0.000624	0.008117	0.004937	0.000916	0.002784	0.014321	0.008871	0.003808	-0.005125	0.003032	-0.001531
2	-0.012793	0.025319	0.004520	5.304264e-04	0.005309	-0.030145	0.008713	0.039265	-0.004062	-0.016231	-0.015653	-0.028472	-0.004704	0.003203	0.003589	-0.014580	3.790047e-04	-0.029135	-0.006013	-0.033785	0.010427	0.009020	0.002954	-0.008326	-0.007213	-0.000114	-0.020657	-0.015482	-0.021392	0.001127	0.016274	0.002995	1.046101e-03	-0.014594	-0.006680	0.016914	0.006251	-0.018416	-0.010446	-0.035941	-0.000731	-0.016865	-0.005815	0.000331	0.017991	-0.010731	-0.020461	2.621187e-03	0.008665	0.017051	0.006704	-0.018906	-0.009744	-0.008969	-0.014305	0.012018	0.006396	-0.003570	-0.020403	1.408939e-03	0.008462	0.005094	-0.004121	-0.000851	-0.022701	0.018504	0.000771	0.016650	-0.025287	0.026366	-0.014155	0.013495	0.027343	-0.005023	0.019760	0.010480	-0.001107	-0.005537	-0.016177	0.006005	-0.014141	-0.004454	-0.021435	0.029012	-0.000441	0.004842	-0.001216	0.024746	0.024719	0.002038	0.018090	0.012996	0.006681	0.008585	0.032990	0.017581	0.007786	-0.013036	0.007046	-0.004856
3	-0.011707	0.018641	0.003150	1.778771e-03	0.002958	-0.024140	0.006620	0.030814	-0.003727	-0.011905	-0.011832	-0.023330	-0.004878	0.003442	0.001128	-0.010880	2.317167e-03	-0.023184	-0.003575	-0.025774	0.010460	0.007362	0.004249	-0.005199	-0.004238	-0.001020	-0.014499	-0.011223	-0.017397	0.001415	0.013044	0.002379	1.156331e-03	-0.012575	-0.005991	0.013978	0.005609	-0.015076	-0.009224	-0.027672	-0.001102	-0.012801	-0.004247	0.000715	0.015762	-0.007338	-0.015917	3.893776e-03	0.008156	0.014522	0.004045	-0.013565	-0.006536	-0.007941	-0.011455	0.007076	0.003198	-0.003424	-0.018024	1.535381e-03	0.004622	0.005443	-0.002419	-0.000951	-0.018184	0.012011	0.000173	0.011245	-0.018527	0.019838	-0.012345	0.010461	0.019350	-0.004775	0.015681	0.006620	-0.001624	-0.004557	-0.013483	0.004999	-0.009452	-0.004801	-0.016549	0.021931	0.001057	0.003109	-0.003504	0.019998	0.018446	0.002802	0.017098	0.011941	0.004078	0.005275	0.026133	0.015409	0.003263	-0.012272	0.006200	-0.004178
4	-0.011816	0.019434	0.004131	2.389939e-04	0.002910	-0.025329	0.007875	0.031003	-0.002305	-0.010869	-0.011833	-0.025239	-0.003048	0.002948	0.002905	-0.012321	8.100063e-05	-0.024542	-0.002162	-0.026533	0.008605	0.008451	0.004306	-0.004930	-0.006728	-0.001483	-0.016250	-0.013289	-0.018622	0.001806	0.015666	0.001920	1.779307e-03	-0.010999	-0.005572	0.013924	0.004675	-0.014140	-0.008148	-0.028909	-0.000835	-0.014566	-0.005893	0.001555	0.014564	-0.007188	-0.016832	3.273971e-03	0.006898	0.014650	0.004633	-0.015392	-0.005702	-0.008732	-0.011834	0.009118	0.004207	-0.003571	-0.019466	-6.173586e-05	0.006025	0.004844	-0.001531	-0.000552	-0.020959	0.014682	-0.001618	0.011850	-0.019411	0.021403	-0.012437	0.010823	0.022361	-0.005199	0.016061	0.007948	-0.001533	-0.005829	-0.013918	0.004772	-0.011791	-0.004903	-0.016238	0.024420	-0.000007	0.003252	-0.000737	0.021313	0.018861	0.001641	0.016667	0.011754	0.005209	0.006025	0.028057	0.016266	0.006836	-0.011025	0.004237	-0.007593
5	-0.004267	0.011806	0.001642	-4.490014e-05	0.002468	-0.013536	0.002101	0.017430	-0.002251	-0.008080	-0.007876	-0.014717	-0.003327	0.001013	0.004064	-0.005383	1.234085e-03	-0.015155	-0.000924	-0.010860	0.004355	0.005013	0.004018	-0.003528	-0.001827	-0.001098	-0.009589	-0.007583	-0.010451	0.000248	0.007337	0.003979	-8.141266e-04	-0.005126	-0.003262	0.008280	0.003046	-0.009144	-0.004612	-0.013740	-0.000043	-0.007735	-0.001590	0.002334	0.007697	-0.007417	-0.009500	2.073694e-03	0.005987	0.009719	0.003513	-0.006868	-0.001765	-0.004797	-0.006648	0.004731	0.000437	-0.003654	-0.011922	-1.531926e-03	0.002324	0.003484	-0.000550	0.001497	-0.009313	0.007196	-0.001568	0.006059	-0.008052	0.012792	-0.007362	0.008539	0.012220	-0.000868	0.007447	0.006006	-0.001315	-0.003230	-0.004742	0.003689	-0.006144	-0.001518	-0.009079	0.014224	-0.000687	0.001692	-0.000371	0.010611	0.009232	0.002771	0.009300	0.004476	0.001691	0.001880	0.014898	0.011124	0.000882	-0.006176	0.002365	-0.001906
6	-0.009258	0.021505	0.001952	1.570783e-03	0.003049	-0.026487	0.007797	0.035735	-0.003372	-0.013669	-0.009910	-0.025864	-0.005456	0.002236	0.002364	-0.011099	1.880894e-03	-0.027536	-0.004571	-0.028748	0.010313	0.008048	0.004670	-0.006722	-0.006728	-0.002644	-0.015966	-0.013558	-0.020285	0.002736	0.015769	0.003074	8.219502e-04	-0.012714	-0.006345	0.016975	0.009111	-0.019215	-0.009762	-0.030441	-0.000183	-0.015467	-0.006631	0.001955	0.019048	-0.007348	-0.016307	1.553537e-03	0.006944	0.016778	0.004613	-0.014467	-0.009193	-0.007612	-0.012767	0.009691	0.003890	-0.004771	-0.020492	-9.673988e-04	0.006747	0.006089	-0.002051	-0.003072	-0.019203	0.016526	-0.000070	0.012424	-0.020499	0.022153	-0.011178	0.013731	0.022129	-0.005447	0.019465	0.009162	-0.000669	-0.003292	-0.014411	0.003261	-0.012555	-0.003421	-0.016548	0.027361	-0.001469	0.004479	-0.002503	0.021387	0.022208	0.001098	0.017032	0.012913	0.004378	0.005647	0.030672	0.019107	0.005822	-0.015678	0.008810	-0.006539
7	-0.006995	0.013414	0.004124	2.141335e-03	0.001388	-0.018244	0.003768	0.021376	-0.002794	-0.007806	-0.009903	-0.018758	-0.002806	0.003213	0.000234	-0.009115	1.328455e-03	-0.017049	-0.000920	-0.018960	0.008081	0.006493	0.003202	-0.002881	-0.003257	-0.000971	-0.011107	-0.007870	-0.013995	0.000514	0.011089	0.003713	-6.761043e-04	-0.008032	-0.001716	0.010084	0.004704	-0.011108	-0.006981	-0.019929	0.000980	-0.009996	-0.002702	0.001845	0.011714	-0.001712	-0.011813	3.479554e-04	0.006174	0.007739	0.005116	-0.011814	-0.004922	-0.005438	-0.008809	0.005319	0.003354	-0.002201	-0.013810	-1.592775e-03	0.003359	0.004055	-0.001860	0.001407	-0.012598	0.009388	0.001382	0.008799	-0.013020	0.015037	-0.010105	0.007178	0.014685	-0.003951	0.010709	0.006090	-0.000275	-0.002997	-0.008698	0.004652	-0.007450	-0.003508	-0.011029	0.015706	-0.001242	0.003370	-0.002198	0.014327	0.011683	0.001379	0.013035	0.007661	0.000885	0.003177	0.018204	0.012084	0.003970	-0.007699	0.005335	-0.002956
8	-0.006992	0.015144	0.004813	-3.746817e-04	0.002714	-0.020848	0.008138	0.026767	-0.003648	-0.008208	-0.009253	-0.019154	-0.005052	0.001316	0.000801	-0.006985	3.883052e-03	-0.019422	-0.002733	-0.019830	0.008117	0.006506	0.001810	-0.003878	-0.005513	0.001562	-0.014040	-0.011909	-0.011668	0.004008	0.010159	0.003133	1.853106e-04	-0.007549	-0.005497	0.011814	0.003289	-0.010493	-0.006163	-0.021294	0.001012	-0.011252	-0.004675	-0.000352	0.010880	-0.005572	-0.012185	1.422174e-03	0.006526	0.013709	0.003774	-0.014484	-0.002471	-0.003753	-0.009099	0.009533	0.003327	-0.002911	-0.012313	5.330618e-03	0.002944	0.003895	-0.003694	0.001248	-0.014721	0.012447	-0.002344	0.008069	-0.016594	0.018004	-0.010107	0.006934	0.016340	-0.005111	0.011581	0.005138	0.000147	-0.004759	-0.010519	0.003452	-0.011421	-0.003360	-0.016465	0.020166	-0.002550	0.002920	0.000743	0.016982	0.014313	0.000584	0.010290	0.007921	0.003450	0.004195	0.020456	0.012332	0.005471	-0.009850	0.003640	-0.003665
9	-0.008169	0.017694	0.005250	-1.597272e-03	0.001313	-0.022292	0.004911	0.030856	-0.003976	-0.013265	-0.010296	-0.021217	-0.002611	0.002159	0.002066	-0.009790	-7.811447e-04	-0.021281	-0.002648	-0.022663	0.007913	0.006643	0.001629	-0.002880	-0.004940	-0.002316	-0.014490	-0.013742	-0.013940	0.000597	0.013182	0.002828	1.822905e-03	-0.010866	-0.005315	0.013542	0.003051	-0.013926	-0.006734	-0.024866	0.001660	-0.012174	-0.005534	0.002119	0.012869	-0.006577	-0.014461	4.047498e-03	0.006852	0.012994	0.007834	-0.013354	-0.005585	-0.007074	-0.009893	0.007675	0.004130	-0.001688	-0.015322	-1.708727e-03	0.003753	0.006326	-0.002432	0.000978	-0.017477	0.012661	0.000173	0.009442	-0.017345	0.018903	-0.011032	0.009802	0.019644	-0.006055	0.017190	0.008340	-0.002667	-0.003941	-0.012072	0.004935	-0.009871	-0.002780	-0.015381	0.022826	0.000979	0.001925	-0.002289	0.017573	0.017133	0.003148	0.013890	0.010278	0.004847	0.004917	0.024359	0.013795	0.004133	-0.012996	0.004150	-0.005846
10	-0.008982	0.016050	0.003531	-5.445541e-04	0.001101	-0.017239	0.006876	0.024208	-0.001278	-0.010310	-0.008153	-0.018641	-0.003924	0.001969	0.001126	-0.007058	-1.142111e-03	-0.017742	-0.002944	-0.021329	0.007661	0.006900	0.003543	-0.005812	-0.005888	0.000324	-0.012111	-0.011350	-0.014351	0.001988	0.012307	0.003197	5.966856e-04	-0.008040	-0.001811	0.008883	0.004626	-0.011561	-0.008233	-0.020975	0.001029	-0.012227	-0.006802	0.001928	0.013438	-0.006869	-0.012452	9.209964e-04	0.007119	0.012433	0.001874	-0.011714	-0.006378	-0.006980	-0.010670	0.007124	0.000385	-0.001437	-0.015288	-1.027619e-03	0.003992	0.003177	-0.000383	0.000416	-0.014420	0.009644	-0.001400	0.009888	-0.015205	0.019046	-0.010038	0.008164	0.016872	-0.003589	0.013105	0.006235	0.000363	-0.005965	-0.011910	0.004217	-0.010132	-0.005224	-0.013108	0.018734	-0.000006	0.001015	-0.001639	0.018270	0.015094	0.002055	0.013483	0.009068	0.004499	0.005189	0.021406	0.011507	0.006313	-0.008766	0.006659	-0.005119
11	-0.006305	0.011825	0.003780	-2.632179e-03	0.002805	-0.016271	0.003042	0.018219	0.000300	-0.006156	-0.007330	-0.014934	-0.003639	0.001299	0.001291	-0.005681	-2.009416e-03	-0.013977	-0.001450	-0.015489	0.006041	0.004368	0.001602	-0.003070	-0.004164	0.000557	-0.010416	-0.008969	-0.010546	0.001253	0.007225	0.000980	-8.928732e-04	-0.007089	-0.004171	0.007950	0.002089	-0.010005	-0.004327	-0.018031	0.000617	-0.006706	-0.004542	-0.000894	0.009583	-0.006389	-0.008977	2.278639e-03	0.005809	0.009054	0.003768	-0.009616	-0.004329	-0.004858	-0.006096	0.004352	0.002457	-0.001201	-0.011376	5.378785e-05	0.002068	0.002462	-0.001162	0.001343	-0.011574	0.009001	0.000307	0.008551	-0.010474	0.011898	-0.008785	0.004483	0.012775	-0.003616	0.009317	0.005383	-0.001449	-0.002950	-0.007162	0.003776	-0.005016	-0.004605	-0.009217	0.015500	-0.000511	0.002914	-0.000841	0.012887	0.012200	0.001137	0.007217	0.005626	0.001620	0.003252	0.017060	0.011762	0.003426	-0.006650	0.002901	-0.003614
12	-0.007249	0.015724	0.002339	-3.666666e-04	0.002627	-0.020874	0.005998	0.029513	-0.004132	-0.011526	-0.010377	-0.020535	-0.002804	0.003935	0.001199	-0.010512	6.589682e-04	-0.020636	-0.003302	-0.022231	0.008112	0.004129	0.003299	-0.004259	-0.005208	-0.002114	-0.013983	-0.012578	-0.017236	0.000651	0.014219	0.003509	-1.482118e-03	-0.010922	-0.003123	0.012314	0.004107	-0.010454	-0.005989	-0.023875	0.000903	-0.014870	-0.003417	0.001875	0.012499	-0.006205	-0.012632	1.175983e-03	0.006192	0.012334	0.005751	-0.013089	-0.005825	-0.008703	-0.009200	0.008950	0.003290	-0.001585	-0.016810	-9.273592e-04	0.004628	0.005751	-0.001346	-0.000306	-0.017567	0.012497	0.002271	0.012912	-0.015402	0.020586	-0.011729	0.009853	0.018626	-0.003353	0.014989	0.007576	-0.000456	-0.003227	-0.011721	0.007029	-0.007695	-0.002900	-0.016514	0.020806	-0.003468	0.002072	-0.003105	0.017596	0.018998	0.002506	0.015017	0.008522	0.005615	0.004836	0.022940	0.012940	0.004313	-0.009643	0.006398	-0.003341
13	-0.011132	0.019704	0.004704	4.163593e-04	0.002379	-0.023820	0.006839	0.029294	-0.004498	-0.011664	-0.010667	-0.022726	-0.004734	0.002404	0.000801	-0.010975	-1.735370e-04	-0.022006	-0.002507	-0.025440	0.007899	0.008003	0.003537	-0.005404	-0.006559	-0.000912	-0.014198	-0.012579	-0.015694	0.001928	0.013301	0.002121	4.552735e-04	-0.010713	-0.006451	0.012633	0.004168	-0.012655	-0.007050	-0.025909	0.000953	-0.013544	-0.005351	0.000571	0.013963	-0.007019	-0.015309	8.535592e-04	0.006595	0.013493	0.006150	-0.015179	-0.004953	-0.006613	-0.011653	0.005768	0.003769	-0.004442	-0.015977	4.500354e-04	0.004508	0.003217	-0.000905	-0.001470	-0.017016	0.014484	-0.001035	0.010507	-0.017476	0.020757	-0.012165	0.010583	0.021059	-0.004825	0.014819	0.007327	-0.001531	-0.003681	-0.012106	0.004663	-0.009521	-0.005346	-0.014212	0.021581	-0.000249	0.002014	-0.001044	0.021203	0.015719	0.001328	0.013439	0.011825	0.005331	0.005413	0.026917	0.012943	0.005635	-0.010576	0.007404	-0.005389
14	-0.006795	0.013934	0.003095	1.450474e-04	0.001688	-0.019159	0.004026	0.023845	-0.005836	-0.005951	-0.010711	-0.015913	-0.003309	0.002106	0.002421	-0.006387	3.140667e-04	-0.014573	-0.005045	-0.018411	0.006781	0.002841	0.001708	-0.003758	-0.004800	-0.000790	-0.008808	-0.011146	-0.013779	-0.000175	0.010295	0.001974	-8.103151e-05	-0.009674	-0.000137	0.009607	0.003213	-0.008283	-0.005581	-0.019975	0.000318	-0.011656	-0.002740	0.000967	0.011496	-0.005008	-0.009927	7.395222e-04	0.004672	0.010730	0.005002	-0.011275	-0.003551	-0.004040	-0.007819	0.004362	0.001487	-0.002991	-0.015235	2.482517e-03	0.004912	0.004247	-0.001380	-0.001091	-0.013376	0.010956	0.001503	0.007977	-0.012015	0.017050	-0.008771	0.006515	0.013956	-0.005378	0.012521	0.004907	0.002069	-0.001882	-0.010280	0.003056	-0.007756	-0.006109	-0.012126	0.017841	-0.001410	0.001973	-0.002522	0.015010	0.015765	0.002278	0.011071	0.007910	0.004625	0.004230	0.019084	0.011606	0.005668	-0.008892	0.003228	-0.003951
15	-0.009570	0.013731	0.001782	1.319855e-03	0.001795	-0.017376	0.003697	0.024987	-0.003590	-0.009950	-0.008639	-0.020144	-0.002961	0.003355	-0.000141	-0.005396	1.519743e-03	-0.018395	-0.003369	-0.019301	0.007663	0.004751	0.002950	-0.005171	-0.002396	-0.001825	-0.011871	-0.010376	-0.013891	-0.000709	0.012004	0.002807	4.213918e-04	-0.007842	-0.001728	0.011394	0.004750	-0.012336	-0.005951	-0.019411	0.002314	-0.011562	-0.004645	0.003225	0.012360	-0.004560	-0.011915	5.349076e-04	0.005817	0.011329	0.006040	-0.012032	-0.005331	-0.004763	-0.008744	0.008366	0.001739	-0.002746	-0.015869	5.106871e-05	0.003835	0.006508	-0.002942	0.000401	-0.013419	0.011074	0.001797	0.010187	-0.012828	0.017696	-0.011463	0.007131	0.017092	-0.005293	0.013891	0.005559	-0.000254	-0.004944	-0.010134	0.002714	-0.010378	-0.004030	-0.011762	0.019879	-0.001486	0.002371	-0.003073	0.013479	0.014965	0.002125	0.012949	0.008383	0.002707	0.003069	0.021475	0.011438	0.005745	-0.008940	0.001308	-0.002321
16	-0.011447	0.021344	0.004635	1.082407e-03	0.004036	-0.028201	0.009475	0.037268	-0.005388	-0.015201	-0.014846	-0.029670	-0.004396	0.003915	0.003747	-0.012755	-3.655478e-04	-0.028164	-0.002646	-0.033316	0.012588	0.009344	0.005134	-0.005008	-0.006352	-0.000890	-0.018327	-0.015065	-0.019788	0.001468	0.017854	0.003797	2.457575e-03	-0.015060	-0.007569	0.017556	0.005538	-0.017079	-0.012490	-0.033916	-0.000215	-0.016214	-0.004964	0.004246	0.016952	-0.008732	-0.019951	2.122512e-03	0.010472	0.018251	0.005255	-0.017525	-0.007644	-0.008928	-0.012975	0.009095	0.002897	-0.004412	-0.023228	9.062263e-04	0.005845	0.006533	-0.003056	0.001119	-0.022974	0.017648	0.000842	0.015308	-0.021603	0.025623	-0.014729	0.012654	0.024873	-0.005744	0.019768	0.009673	-0.000390	-0.005316	-0.015227	0.006678	-0.012700	-0.005273	-0.020327	0.029463	-0.000296	0.003919	-0.001869	0.026035	0.022914	0.002078	0.019875	0.012460	0.007148	0.006552	0.033150	0.020201	0.006472	-0.014266	0.006773	-0.006473
17	-0.005951	0.010614	0.000412	-1.102232e-03	0.001289	-0.011812	0.001124	0.015110	-0.002078	-0.003777	-0.007731	-0.010485	-0.001882	0.003755	0.001685	-0.002983	2.159761e-04	-0.009808	-0.003525	-0.013305	0.005178	0.001794	-0.001533	-0.000652	-0.002156	-0.000671	-0.007922	-0.008234	-0.007558	0.000543	0.005770	0.003641	-5.976632e-04	-0.005166	-0.000413	0.005306	0.004832	-0.006974	-0.003402	-0.011724	0.000045	-0.009726	-0.003062	0.001122	0.005864	-0.001830	-0.007249	3.558680e-04	0.002091	0.006027	0.003579	-0.005846	-0.005194	-0.001390	-0.007404	0.004122	-0.001414	-0.002467	-0.008886	2.984589e-03	0.002062	0.004910	-0.002302	-0.000300	-0.007957	0.007561	-0.000178	0.006927	-0.007705	0.011475	-0.006397	0.001889	0.009829	-0.006735	0.008537	0.005227	0.000073	-0.004111	-0.005898	0.001669	-0.004263	-0.003447	-0.008597	0.011233	0.002398	0.001765	-0.003809	0.009875	0.010354	0.001076	0.005555	0.004497	0.001167	0.001257	0.013770	0.009443	0.004411	-0.005456	0.000792	-0.001988
18	-0.005558	0.013329	0.003422	-3.967324e-04	0.004000	-0.020918	0.006111	0.024099	-0.001947	-0.009177	-0.011753	-0.019032	-0.004096	0.002481	0.000082	-0.009095	1.783995e-04	-0.018130	-0.001503	-0.020737	0.006392	0.006291	0.003512	-0.001619	-0.004221	-0.000830	-0.011221	-0.011736	-0.014184	0.001526	0.012550	0.001143	2.650189e-03	-0.009133	-0.003040	0.008927	0.001424	-0.010861	-0.005874	-0.020915	0.000092	-0.010173	-0.003446	0.001434	0.010963	-0.005424	-0.011208	-3.387009e-04	0.006293	0.013440	0.002362	-0.012062	-0.002823	-0.005720	-0.008643	0.006500	0.001779	-0.001248	-0.014385	-4.945476e-04	0.003901	0.003476	-0.002618	-0.000939	-0.014241	0.008559	0.003098	0.007465	-0.014055	0.017277	-0.009884	0.008183	0.016453	-0.004098	0.014565	0.005081	0.000524	-0.002993	-0.010385	0.005311	-0.009849	-0.002354	-0.012718	0.016960	-0.001775	0.001503	-0.000203	0.016504	0.014867	0.002613	0.013789	0.007128	0.005776	0.006143	0.020576	0.012089	0.006405	-0.007933	0.005534	-0.004804
19	-0.008489	0.017948	0.003364	1.077579e-03	0.002090	-0.020431	0.004758	0.026497	-0.004327	-0.011031	-0.009669	-0.020359	-0.001804	0.002772	0.000239	-0.008996	1.623243e-03	-0.020524	-0.001711	-0.022154	0.008188	0.006025	0.006480	-0.003587	-0.008286	-0.000128	-0.013398	-0.012587	-0.014461	0.002602	0.013009	0.004563	3.532393e-03	-0.008797	-0.004840	0.010409	0.003966	-0.013165	-0.010075	-0.024723	-0.000043	-0.012225	-0.004492	0.000703	0.011446	-0.007846	-0.014208	-1.561194e-03	0.006885	0.013201	0.005366	-0.012890	-0.007312	-0.007523	-0.009253	0.008780	0.002612	-0.003492	-0.018382	6.224403e-04	0.006247	0.003106	-0.004634	-0.000599	-0.019003	0.011223	-0.001213	0.007727	-0.016473	0.018688	-0.008761	0.008627	0.018084	-0.004266	0.014888	0.006850	0.000018	-0.002877	-0.013790	0.006753	-0.008211	-0.002612	-0.014189	0.021380	-0.001936	0.002453	-0.002094	0.018739	0.018444	0.002135	0.014385	0.008724	0.003889	0.003606	0.022486	0.013539	0.004140	-0.010895	0.006525	-0.006772
20	-0.010410	0.018718	0.002590	-2.758139e-04	0.001608	-0.022193	0.006104	0.028439	-0.002769	-0.010091	-0.009837	-0.019839	-0.003594	0.003193	0.002674	-0.009518	-1.758727e-03	-0.022546	-0.003301	-0.023445	0.006810	0.009006	0.004418	-0.001650	-0.005557	-0.000487	-0.014555	-0.011377	-0.013200	-0.000392	0.014824	0.003050	-7.890640e-04	-0.012353	-0.001572	0.012422	0.004834	-0.010706	-0.007687	-0.023747	-0.000985	-0.012839	-0.003718	-0.000428	0.012425	-0.006443	-0.015240	1.143750e-03	0.007467	0.013349	0.007373	-0.012842	-0.006118	-0.007158	-0.012458	0.006993	0.005272	-0.003419	-0.018656	1.755876e-03	0.005002	0.004783	-0.002094	-0.001546	-0.015957	0.012317	0.001365	0.012325	-0.018131	0.019136	-0.010509	0.008962	0.019189	-0.004790	0.013503	0.004142	-0.000667	-0.004011	-0.013060	0.007287	-0.010434	-0.004066	-0.013600	0.019073	-0.002243	0.004085	-0.001095	0.019528	0.017071	0.002420	0.013802	0.010484	0.006104	0.004770	0.025186	0.015553	0.005554	-0.010841	0.004864	-0.005501
21	-0.011090	0.019734	0.004444	-2.471985e-03	0.002184	-0.026883	0.006275	0.035349	-0.003138	-0.012659	-0.014707	-0.028221	-0.005627	0.003634	0.001630	-0.011590	-1.293146e-03	-0.026280	-0.003842	-0.029245	0.010429	0.008935	0.001943	-0.004250	-0.005787	0.000652	-0.016761	-0.014789	-0.019221	0.000908	0.016126	0.001445	-6.303524e-05	-0.012956	-0.008237	0.013919	0.007897	-0.017943	-0.007148	-0.032602	-0.000550	-0.018010	-0.006364	0.001273	0.016897	-0.008387	-0.016811	2.078933e-03	0.008831	0.016906	0.005068	-0.017471	-0.006065	-0.008271	-0.012113	0.011445	0.003915	-0.005978	-0.021850	1.314678e-03	0.008265	0.005910	-0.003235	0.001152	-0.020182	0.018157	0.000539	0.013519	-0.018876	0.023430	-0.014645	0.015219	0.024294	-0.006225	0.018777	0.008947	-0.000122	-0.005335	-0.014360	0.004453	-0.009144	-0.005377	-0.018189	0.027423	-0.000006	0.002634	-0.002224	0.022265	0.020615	0.002732	0.018390	0.013558	0.005352	0.006300	0.028413	0.018072	0.007530	-0.013747	0.005850	-0.007841
22	-0.009091	0.014005	0.004817	1.843427e-03	0.001545	-0.020173	0.003788	0.026470	-0.004234	-0.007793	-0.008822	-0.018954	-0.004931	0.004197	-0.000292	-0.008819	-1.662256e-03	-0.018057	-0.001681	-0.019498	0.007307	0.005662	0.004494	-0.001499	-0.005355	0.000100	-0.011712	-0.011684	-0.013527	0.001602	0.011059	0.002701	2.173630e-03	-0.009470	-0.002255	0.009863	0.003108	-0.011734	-0.005708	-0.021697	0.001439	-0.011747	-0.005542	0.001372	0.011615	-0.005774	-0.014333	2.215402e-03	0.008035	0.010628	0.004629	-0.011555	-0.006938	-0.006763	-0.007489	0.005100	0.003013	-0.005246	-0.014708	1.023585e-03	0.005408	0.003169	-0.000905	-0.000026	-0.016278	0.010636	0.001383	0.011882	-0.013509	0.017560	-0.009718	0.007306	0.016951	-0.003200	0.013436	0.004805	-0.001977	-0.001746	-0.009058	0.002375	-0.005830	-0.005612	-0.013334	0.015550	-0.002401	0.003081	-0.000753	0.014726	0.014362	0.003281	0.014033	0.009217	0.005114	0.005682	0.019537	0.011430	0.002061	-0.007613	0.005766	-0.003903
23	-0.014289	0.024804	0.004096	2.547541e-04	0.003858	-0.030539	0.010111	0.039018	-0.003070	-0.014399	-0.012955	-0.029043	-0.005423	0.003831	0.002346	-0.012308	1.088836e-03	-0.026776	-0.003747	-0.032826	0.012100	0.010823	0.005540	-0.007524	-0.004800	-0.000305	-0.016458	-0.016694	-0.021385	0.002301	0.015519	0.002339	2.037568e-03	-0.015014	-0.006557	0.015940	0.009604	-0.015925	-0.008132	-0.033666	0.000921	-0.015790	-0.006640	0.001648	0.019857	-0.006155	-0.020637	2.600396e-03	0.010663	0.016874	0.007899	-0.017772	-0.008392	-0.009061	-0.012820	0.011104	0.005286	-0.004430	-0.023530	9.294949e-04	0.003842	0.008662	-0.002756	-0.000913	-0.022520	0.018983	0.000617	0.015095	-0.023017	0.025363	-0.015444	0.012105	0.026317	-0.005121	0.020050	0.009384	-0.001621	-0.006958	-0.017894	0.007047	-0.014377	-0.006190	-0.020597	0.029563	0.001883	0.005769	-0.000891	0.022388	0.024034	0.002959	0.019954	0.012561	0.008567	0.007308	0.034544	0.021092	0.007606	-0.014253	0.006269	-0.008143
24	-0.009055	0.017014	0.003198	-1.211346e-03	0.002182	-0.022897	0.004348	0.028689	-0.003057	-0.011742	-0.011569	-0.020510	-0.002441	0.001210	0.000445	-0.010258	4.281836e-04	-0.022766	-0.002835	-0.024230	0.009668	0.006252	0.002139	-0.003672	-0.008048	-0.001479	-0.015910	-0.012094	-0.014704	0.001532	0.014625	0.003595	2.625890e-03	-0.011079	-0.005131	0.012812	0.005337	-0.013409	-0.007544	-0.026566	0.001010	-0.012809	-0.006345	0.001718	0.012933	-0.006355	-0.012178	2.278927e-03	0.006634	0.014246	0.004874	-0.013452	-0.005414	-0.007301	-0.009928	0.009104	0.003647	-0.002950	-0.016770	-1.733591e-03	0.003154	0.005434	-0.004910	0.001261	-0.017908	0.013270	0.001060	0.008823	-0.015535	0.020016	-0.012415	0.008734	0.018938	-0.004822	0.016010	0.007610	-0.002008	-0.004245	-0.012624	0.005803	-0.008731	-0.005276	-0.016077	0.023159	0.000047	0.002279	-0.002622	0.019100	0.017346	0.003596	0.014148	0.010630	0.004629	0.004911	0.023681	0.013102	0.002919	-0.012184	0.004784	-0.004404
25	-0.009514	0.020349	0.003979	-1.953063e-03	0.001434	-0.026657	0.005507	0.035856	-0.006057	-0.012775	-0.012913	-0.026022	-0.006110	0.004516	0.001663	-0.012406	-1.268689e-03	-0.024713	-0.003010	-0.030199	0.009817	0.006976	0.003855	-0.004341	-0.007448	-0.002445	-0.017523	-0.014657	-0.018640	0.001361	0.015811	0.001788	3.755256e-03	-0.012708	-0.005976	0.017070	0.006126	-0.015353	-0.008288	-0.032342	0.001364	-0.017416	-0.006412	0.001543	0.015960	-0.008894	-0.017016	2.592591e-03	0.007905	0.016085	0.006387	-0.016575	-0.006062	-0.008736	-0.011724	0.009720	0.004170	-0.004931	-0.020560	6.998313e-04	0.004252	0.005356	-0.003768	-0.000615	-0.022379	0.015956	0.002917	0.012952	-0.018573	0.024221	-0.014616	0.012510	0.023990	-0.005385	0.018980	0.010585	-0.000309	-0.006347	-0.012963	0.005694	-0.009936	-0.005313	-0.018037	0.028385	-0.000091	0.003690	-0.003315	0.021924	0.023108	0.003180	0.017482	0.012544	0.004896	0.006355	0.029645	0.018364	0.006412	-0.014157	0.007777	-0.007153
26	-0.007990	0.013384	0.005363	-6.114652e-04	-0.000195	-0.017140	0.003941	0.022517	-0.003447	-0.009334	-0.009004	-0.018267	-0.001533	0.003251	0.001558	-0.008952	-1.179441e-03	-0.017636	-0.002491	-0.019291	0.005905	0.005610	0.002345	-0.003114	-0.003601	-0.001090	-0.011386	-0.008241	-0.011412	0.000570	0.009235	0.003506	4.125084e-04	-0.007493	-0.003635	0.009096	0.002828	-0.011282	-0.008378	-0.020470	0.001709	-0.009415	-0.003839	0.001119	0.011117	-0.004928	-0.011673	2.026228e-04	0.002535	0.008252	0.004407	-0.011289	-0.007008	-0.005750	-0.006926	0.006478	0.002711	-0.002856	-0.014991	3.882699e-04	0.001170	0.002369	-0.001707	-0.000664	-0.012921	0.008152	0.002297	0.009107	-0.013908	0.015115	-0.008227	0.008801	0.014498	-0.004700	0.012024	0.005256	-0.001053	-0.004896	-0.011331	0.005399	-0.007251	-0.002595	-0.013590	0.018607	-0.001376	0.003019	-0.001852	0.014168	0.015618	-0.001402	0.011245	0.007317	0.002071	0.003500	0.018132	0.011508	0.004215	-0.010539	0.004526	-0.004488
27	-0.010404	0.020316	0.004443	-2.704903e-04	0.003042	-0.024452	0.008414	0.031722	-0.005609	-0.011614	-0.013601	-0.023887	-0.003908	0.002870	0.000276	-0.011875	-3.832331e-04	-0.023834	-0.005874	-0.028240	0.008284	0.008192	0.002866	-0.005654	-0.006836	-0.000899	-0.015333	-0.013223	-0.017673	0.001723	0.015566	0.002514	7.576524e-04	-0.011418	-0.005794	0.014371	0.007662	-0.015322	-0.007757	-0.028681	0.003357	-0.014948	-0.004865	0.001551	0.014490	-0.007628	-0.017625	8.852674e-04	0.009410	0.013254	0.005079	-0.015983	-0.005426	-0.008845	-0.012826	0.008563	0.003577	-0.002957	-0.020271	1.987027e-03	0.005835	0.003936	-0.002818	-0.000499	-0.018778	0.013430	0.003044	0.010590	-0.019546	0.020268	-0.013753	0.008569	0.021374	-0.007272	0.015429	0.008107	-0.001737	-0.003015	-0.013285	0.007741	-0.010603	-0.002830	-0.018000	0.024092	-0.002599	0.004788	-0.001909	0.022908	0.020627	0.003178	0.015621	0.011005	0.005879	0.004303	0.025407	0.015618	0.006635	-0.012547	0.006798	-0.006040
28	-0.018066	0.031364	0.006642	-1.110790e-03	0.002297	-0.039746	0.012146	0.050873	-0.007091	-0.020711	-0.021457	-0.037128	-0.005667	0.002883	0.000866	-0.017914	1.179959e-03	-0.037111	-0.003858	-0.045008	0.014459	0.011363	0.006324	-0.006528	-0.009447	-0.001141	-0.025009	-0.022322	-0.027980	0.002507	0.024054	0.005714	3.743248e-03	-0.017563	-0.008150	0.022484	0.006818	-0.025137	-0.013720	-0.047824	0.002356	-0.022671	-0.009571	0.001159	0.024250	-0.013713	-0.026435	1.384342e-03	0.014412	0.022064	0.009305	-0.023632	-0.009484	-0.011572	-0.017054	0.014912	0.006468	-0.005472	-0.030364	2.423025e-03	0.008978	0.004497	-0.005239	-0.001827	-0.029298	0.023449	0.003314	0.018968	-0.027720	0.034905	-0.021317	0.018149	0.033327	-0.007422	0.026887	0.014280	0.001887	-0.005401	-0.020914	0.007006	-0.017190	-0.006314	-0.025989	0.039427	-0.001082	0.005411	-0.001038	0.033915	0.030059	0.004181	0.025608	0.017012	0.005577	0.010123	0.042104	0.023365	0.011678	-0.018177	0.010499	-0.009889
29	-0.010545	0.020075	0.002416	-1.560805e-03	0.002753	-0.025975	0.007243	0.033907	-0.004750	-0.011538	-0.015410	-0.022985	-0.002273	0.000218	-0.002833	-0.010752	-1.369219e-03	-0.020792	-0.002720	-0.025216	0.008101	0.007584	0.000101	-0.009081	-0.004043	-0.001501	-0.013609	-0.016131	-0.017260	-0.000482	0.014512	0.001349	-4.233783e-04	-0.013704	-0.004020	0.013617	0.004583	-0.015799	-0.009796	-0.027331	0.005880	-0.015369	-0.006501	0.000692	0.016725	-0.005970	-0.012459	3.621203e-03	0.006078	0.013145	0.007627	-0.018236	-0.005346	-0.002967	-0.012287	0.011617	0.002276	-0.004502	-0.020253	3.380402e-03	0.007127	0.006572	-0.003882	0.000006	-0.017709	0.015324	0.003048	0.011605	-0.014880	0.026103	-0.012521	0.011113	0.024259	-0.004855	0.019739	0.006692	-0.001294	-0.006400	-0.014990	0.005520	-0.013020	-0.004362	-0.016899	0.022458	-0.000946	0.001516	-0.001904	0.016937	0.016848	0.000759	0.019288	0.010522	0.007669	0.006874	0.024710	0.016921	0.008969	-0.012704	0.003397	-0.003913
30	-0.009392	0.016744	0.004034	8.278277e-05	0.002071	-0.020883	0.004651	0.027750	-0.004722	-0.010092	-0.012424	-0.021045	-0.004965	0.003177	0.001821	-0.009025	4.563983e-04	-0.021117	-0.002961	-0.020425	0.007272	0.005329	0.003683	-0.004864	-0.005362	0.000144	-0.012169	-0.011188	-0.013857	-0.000060	0.013137	0.002025	7.507082e-04	-0.010624	-0.003056	0.013169	0.005291	-0.014129	-0.006840	-0.022566	0.002195	-0.011409	-0.006860	0.001370	0.012075	-0.008054	-0.012163	5.482177e-04	0.007796	0.010302	0.005985	-0.012845	-0.004895	-0.006810	-0.010574	0.010568	0.003659	-0.005893	-0.014944	-3.234610e-04	0.005631	0.003227	-0.000431	0.000294	-0.014870	0.011352	0.001382	0.010614	-0.015987	0.016993	-0.011742	0.010488	0.019666	-0.003740	0.013932	0.006677	-0.002563	-0.003930	-0.012656	0.003988	-0.008891	-0.005667	-0.014266	0.020132	-0.001000	0.002273	-0.001061	0.018698	0.015976	0.001712	0.013790	0.008342	0.005349	0.003605	0.025075	0.012120	0.004106	-0.008182	0.005483	-0.006814
31	-0.012341	0.024265	0.005216	6.599499e-04	0.002525	-0.030968	0.008301	0.038837	-0.006481	-0.016408	-0.015807	-0.028791	-0.004475	0.003945	0.002584	-0.013169	1.339741e-03	-0.028395	-0.003780	-0.033351	0.012190	0.009565	0.004396	-0.006782	-0.005756	-0.000612	-0.020413	-0.015556	-0.020654	0.002948	0.017947	0.002207	2.170812e-03	-0.015033	-0.005164	0.017190	0.006112	-0.018030	-0.009788	-0.035281	0.002377	-0.016747	-0.006365	0.000720	0.018603	-0.009881	-0.022064	1.641918e-03	0.009321	0.016896	0.005156	-0.019030	-0.006970	-0.008667	-0.013564	0.011972	0.004639	-0.004720	-0.023505	1.733599e-03	0.005650	0.005023	-0.002779	0.000513	-0.021716	0.018753	0.001585	0.015078	-0.023440	0.024539	-0.015965	0.014447	0.024723	-0.006140	0.018645	0.010903	0.000326	-0.004770	-0.016067	0.006243	-0.011893	-0.004437	-0.020417	0.029748	0.000223	0.004361	-0.000169	0.025888	0.022342	0.002527	0.018693	0.013599	0.005075	0.006392	0.032176	0.018529	0.007888	-0.014421	0.008276	-0.006623
32	-0.010330	0.018420	0.005528	-5.660002e-04	0.002877	-0.023790	0.005753	0.031961	-0.005764	-0.014794	-0.010501	-0.021842	-0.005665	0.001948	0.002349	-0.009662	-3.560578e-04	-0.024050	-0.003539	-0.026003	0.008497	0.007372	0.004047	-0.004468	-0.007618	0.000071	-0.013981	-0.014091	-0.018127	0.000598	0.013853	0.004561	2.838179e-03	-0.008523	-0.003981	0.013820	0.005330	-0.015236	-0.009112	-0.026777	0.001778	-0.015966	-0.005733	0.001125	0.012680	-0.008506	-0.017229	2.655141e-03	0.007532	0.014106	0.005225	-0.015607	-0.006754	-0.006296	-0.011497	0.009749	0.004453	-0.003613	-0.017281	-7.556035e-04	0.004815	0.004787	-0.005041	-0.000856	-0.018235	0.012785	-0.000911	0.011944	-0.018156	0.021840	-0.011729	0.010532	0.021122	-0.005274	0.016444	0.007427	0.001145	-0.004837	-0.014680	0.004314	-0.008998	-0.004442	-0.016499	0.023923	0.000303	0.004593	-0.002525	0.019878	0.018504	0.003038	0.017444	0.011674	0.003825	0.006747	0.025476	0.016537	0.005425	-0.011696	0.008003	-0.005846
33	-0.010275	0.016998	0.002714	-2.314849e-03	0.000797	-0.022815	0.005300	0.029408	-0.002870	-0.010912	-0.011145	-0.022137	-0.003340	0.003459	-0.000797	-0.008592	2.532772e-04	-0.022251	-0.001054	-0.020938	0.009247	0.008068	0.004852	-0.001402	-0.003894	-0.000939	-0.012772	-0.012415	-0.014450	0.002700	0.015280	0.003732	2.006818e-03	-0.013540	-0.005651	0.013403	0.004620	-0.012287	-0.006938	-0.025782	-0.000139	-0.010629	-0.003237	0.001461	0.013829	-0.005415	-0.015665	6.675353e-04	0.004436	0.013541	0.004836	-0.012609	-0.005146	-0.005856	-0.010248	0.007832	0.003063	-0.004168	-0.016996	8.602180e-04	0.003992	0.002765	-0.004628	0.001558	-0.014231	0.010478	0.000640	0.009500	-0.014874	0.019860	-0.010244	0.008326	0.018824	-0.003372	0.014365	0.006718	-0.001535	-0.004162	-0.010506	0.005632	-0.008255	-0.004161	-0.014358	0.019435	-0.000161	0.002860	-0.001210	0.019226	0.018240	0.003515	0.012649	0.010522	0.004374	0.004639	0.023331	0.014816	0.004649	-0.009516	0.005742	-0.004554
34	-0.012637	0.022031	0.002114	2.733300e-04	0.008386	-0.027166	0.009588	0.040478	-0.008503	-0.015194	-0.015730	-0.030380	-0.005869	0.005952	0.002613	-0.010229	2.537470e-03	-0.025124	-0.001859	-0.031642	0.010283	0.007514	0.004676	-0.007278	-0.006574	-0.003257	-0.018580	-0.012809	-0.021939	0.001312	0.019865	0.001739	4.288600e-04	-0.015515	-0.005258	0.013340	0.003310	-0.013441	-0.011059	-0.033542	0.000832	-0.016475	-0.006309	0.005159	0.018170	-0.007998	-0.020616	2.090100e-04	0.009966	0.015810	0.008793	-0.017798	-0.007973	-0.010367	-0.014374	0.009862	0.004672	-0.003137	-0.024026	2.226443e-03	0.007185	0.008095	-0.000109	-0.002338	-0.019646	0.019962	0.005406	0.018079	-0.024270	0.028199	-0.013778	0.011690	0.027018	-0.004764	0.020767	0.010949	-0.001499	-0.006785	-0.014405	0.005266	-0.013180	-0.006747	-0.018279	0.026426	0.000081	0.002497	0.000908	0.021736	0.022759	0.001386	0.020008	0.012275	0.006375	0.006350	0.031710	0.017742	0.007196	-0.013778	0.006786	-0.006307
35	-0.008844	0.015518	0.001549	-1.837049e-03	0.003604	-0.019178	0.004425	0.027452	-0.005134	-0.008119	-0.011023	-0.017866	-0.005119	0.003008	0.003794	-0.007182	-1.670345e-04	-0.015715	-0.001906	-0.020504	0.007671	0.005309	0.001493	-0.002915	-0.003596	-0.000998	-0.011234	-0.007442	-0.014555	0.001884	0.012515	0.000101	1.021381e-03	-0.010521	-0.007791	0.010289	0.002625	-0.011509	-0.005920	-0.021461	0.002063	-0.012250	-0.004393	0.001355	0.011115	-0.005491	-0.011243	-4.281872e-04	0.005578	0.010893	0.003194	-0.012234	-0.003498	-0.005002	-0.008912	0.005501	0.003304	-0.001940	-0.014315	5.611006e-04	0.005775	0.003804	-0.002702	0.001668	-0.014512	0.011464	0.001239	0.010760	-0.013521	0.019735	-0.011662	0.008930	0.017584	-0.003466	0.014357	0.007674	-0.002046	-0.002570	-0.011217	0.001403	-0.009181	-0.003104	-0.014040	0.017929	0.000140	0.000106	-0.001998	0.016933	0.016740	0.001651	0.013979	0.009606	0.003582	0.005419	0.021483	0.009992	0.005470	-0.010134	0.004575	-0.003680
36	-0.011719	0.025816	0.005002	-4.010497e-04	0.002698	-0.030469	0.008676	0.040813	-0.004774	-0.014661	-0.015786	-0.032707	-0.004557	0.004420	0.002466	-0.014158	4.480683e-04	-0.030955	-0.004968	-0.036382	0.010080	0.007984	0.002478	-0.005603	-0.008238	-0.000008	-0.020740	-0.016068	-0.021280	0.001719	0.016815	0.002921	2.160937e-03	-0.016030	-0.007738	0.016940	0.008691	-0.019540	-0.010934	-0.036576	-0.000244	-0.019377	-0.007135	0.000341	0.018904	-0.011659	-0.021385	2.863590e-03	0.010153	0.019124	0.005390	-0.019003	-0.008452	-0.010580	-0.014289	0.011234	0.005007	-0.003915	-0.022718	1.162864e-03	0.007133	0.005455	-0.004652	0.001595	-0.023792	0.017784	0.000342	0.015974	-0.023355	0.025570	-0.017934	0.016662	0.026574	-0.006757	0.020516	0.010686	-0.000878	-0.005568	-0.015087	0.007416	-0.012912	-0.006427	-0.020685	0.031775	-0.000694	0.005005	-0.001740	0.027336	0.025964	0.003352	0.020106	0.015429	0.005582	0.006683	0.035823	0.019421	0.009162	-0.014852	0.007388	-0.008184
37	-0.010009	0.016264	0.003728	-2.975090e-03	0.002820	-0.022717	0.005506	0.029567	-0.002740	-0.014402	-0.012801	-0.022596	-0.005870	0.002598	0.002771	-0.010442	7.704777e-04	-0.022804	-0.001833	-0.023421	0.006330	0.006594	0.004016	-0.001056	-0.006079	-0.000368	-0.012723	-0.011769	-0.016186	0.003123	0.012368	0.003746	7.984732e-04	-0.010690	-0.004014	0.011812	0.003590	-0.013602	-0.007144	-0.024941	0.001490	-0.012429	-0.002928	-0.001838	0.015123	-0.006406	-0.014438	1.953043e-03	0.007044	0.013965	0.006015	-0.014238	-0.006991	-0.007305	-0.011442	0.008010	0.004402	-0.005080	-0.018857	-6.282940e-04	0.004520	0.003812	-0.002428	-0.000079	-0.016659	0.011717	0.001826	0.011408	-0.016431	0.019446	-0.011375	0.010418	0.020468	-0.004883	0.013364	0.006412	-0.001728	-0.003275	-0.012285	0.004693	-0.009000	-0.004979	-0.015080	0.022723	-0.000251	0.001258	-0.001294	0.021750	0.017513	0.001302	0.014379	0.010108	0.004695	0.005325	0.026788	0.013739	0.005654	-0.011013	0.004916	-0.004900
38	-0.010009	0.016264	0.003728	-2.975090e-03	0.002820	-0.022717	0.005506	0.029567	-0.002740	-0.014402	-0.012801	-0.022596	-0.005870	0.002598	0.002771	-0.010442	7.704777e-04	-0.022804	-0.001833	-0.023421	0.006330	0.006594	0.004016	-0.001056	-0.006079	-0.000368	-0.012723	-0.011769	-0.016186	0.003123	0.012368	0.003746	7.984732e-04	-0.010690	-0.004014	0.011812	0.003590	-0.013602	-0.007144	-0.024941	0.001490	-0.012429	-0.002928	-0.001838	0.015123	-0.006406	-0.014438	1.953043e-03	0.007044	0.013965	0.006015	-0.014238	-0.006991	-0.007305	-0.011442	0.008010	0.004402	-0.005080	-0.018857	-6.282940e-04	0.004520	0.003812	-0.002428	-0.000079	-0.016659	0.011717	0.001826	0.011408	-0.016431	0.019446	-0.011375	0.010418	0.020468	-0.004883	0.013364	0.006412	-0.001728	-0.003275	-0.012285	0.004693	-0.009000	-0.004979	-0.015080	0.022723	-0.000251	0.001258	-0.001294	0.021750	0.017513	0.001302	0.014379	0.010108	0.004695	0.005325	0.026788	0.013739	0.005654	-0.011013	0.004916	-0.004900
39	-0.005784	0.012633	0.002710	-2.511812e-04	0.003234	-0.015174	0.004680	0.021003	-0.001970	-0.008903	-0.008302	-0.014368	-0.002821	0.001567	-0.001624	-0.007423	3.283426e-04	-0.014959	-0.003347	-0.015967	0.006148	0.004843	0.001282	-0.003670	-0.005892	-0.000008	-0.009093	-0.008977	-0.009172	-0.000053	0.008627	0.001284	8.472202e-04	-0.008943	-0.003382	0.009281	0.001821	-0.008592	-0.004820	-0.017330	0.002996	-0.009233	-0.002437	0.000755	0.008422	-0.005758	-0.011133	4.341745e-04	0.005927	0.009516	0.004453	-0.009074	-0.003035	-0.005556	-0.006479	0.006397	0.003726	-0.002547	-0.013432	-1.098944e-03	0.000828	0.001026	-0.002526	-0.000435	-0.011788	0.009452	-0.000200	0.007357	-0.010680	0.012793	-0.009341	0.005216	0.012509	-0.003092	0.008408	0.004987	-0.000347	-0.002385	-0.008112	0.003965	-0.006572	-0.004513	-0.009478	0.014654	0.000245	0.001985	-0.002642	0.014051	0.012036	0.001515	0.010425	0.007073	0.002185	0.002818	0.016176	0.008909	0.003536	-0.008164	0.002538	-0.002412

	Feature 0	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6	Feature 7	Feature 8	Feature 9	Feature 10	Feature 11	Feature 12	Feature 13	Feature 14	Feature 15	Feature 16	Feature 17	Feature 18	Feature 19	Feature 20	Feature 21	Feature 22	Feature 23	Feature 24	Feature 25	Feature 26	Feature 27	Feature 28	Feature 29	Feature 30	Feature 31	Feature 32	Feature 33	Feature 34	Feature 35	Feature 36	Feature 37	Feature 38	Feature 39	Feature 40	Feature 41	Feature 42	Feature 43	Feature 44	Feature 45	Feature 46	Feature 47	Feature 48	Feature 49	Feature 50	Feature 51	Feature 52	Feature 53	Feature 54	Feature 55	Feature 56	Feature 57	Feature 58	Feature 59	Feature 60	Feature 61	Feature 62	Feature 63	Feature 64	Feature 65	Feature 66	Feature 67	Feature 68	Feature 69	Feature 70	Feature 71	Feature 72	Feature 73	Feature 74	Feature 75	Feature 76	Feature 77	Feature 78	Feature 79	Feature 80	Feature 81	Feature 82	Feature 83	Feature 84	Feature 85	Feature 86	Feature 87	Feature 88	Feature 89	Feature 90	Feature 91	Feature 92	Feature 93	Feature 94	Feature 95	Feature 96	Feature 97	Feature 98	Feature 99
0	-0.104074	0.085043	-0.006874	-0.117856	-0.047997	-0.262621	-0.220739	0.233383	-0.020247	0.153795	0.030064	-0.066967	0.075305	0.021312	0.047142	-0.096180	-0.053975	-0.025514	-0.025976	-0.038412	0.077116	-0.038864	0.027478	-0.189773	0.289805	0.141860	-0.362076	-0.230692	0.068778	-0.038065	-0.129387	0.257542	0.014375	0.148728	-0.108855	0.007931	0.007585	-0.107753	0.133805	-0.140516	-0.189102	-0.282783	0.198150	-0.239388	-0.141640	0.026585	-0.143630	-0.047483	-0.076828	-0.529719	-0.064704	0.093672	-0.132642	0.792717	-0.108362	-1.245691	0.010391	-0.314867	1.299591	0.317367	0.014671	0.214499	0.229937	0.448987	0.371565	0.123599	0.163902	0.077562	0.008870	-0.116395	-0.056969	-0.124979	0.087753	-0.113326	0.152483	0.056962	0.013259	0.141870	-0.229118	-0.039120	0.374872	-0.136357	-0.252443	-0.063580	-0.725687	0.033864	0.317773	0.005882	0.004755	-0.197971	-0.161009	0.104659	-0.117153	0.055421	-0.017000	-0.119250	0.085884	-0.166344	0.460105	0.049004
1	-0.260657	0.222551	-0.037376	0.059230	0.048171	0.049342	-0.025612	0.298147	0.088059	0.113739	0.119546	-0.066295	-0.057286	0.308257	0.196786	-0.181216	-0.042313	-0.024512	-0.180914	-0.190796	-0.032796	-0.109833	-0.124461	0.112727	-0.042324	0.165175	-0.047378	0.003852	0.045869	0.095636	0.147486	0.291462	-0.134003	0.122430	0.082004	0.093402	0.118938	0.098432	0.164193	0.058668	0.030141	-0.154764	-0.168232	-0.335609	0.234516	0.139116	-0.200756	-0.101201	-0.165461	-0.196688	0.000077	0.070191	0.057763	0.589206	-0.277074	-1.300450	-0.334990	-0.217926	0.848220	0.155052	0.077902	0.166048	0.082566	0.320544	0.492050	-0.027020	0.208240	-0.242667	0.129008	-0.418419	-0.140058	-0.250426	0.197868	0.109494	0.137582	0.164373	0.155112	-0.037666	-0.344431	-0.192659	0.174955	0.032303	-0.331479	0.120331	-0.751755	0.279108	0.453428	0.030619	-0.203554	-0.151350	-0.008167	-0.069979	-0.288788	0.023639	-0.156443	-0.020904	0.110019	-0.199682	0.167008	-0.127085
2	-0.069923	0.097646	0.039634	-0.173443	-0.034290	-0.148609	-0.009568	0.094570	-0.053122	0.140734	0.134343	-0.212128	0.363301	0.110846	0.188240	0.000159	0.047959	-0.125856	-0.104772	-0.050605	0.337794	0.234857	-0.045871	-0.052403	0.334046	-0.018769	-0.193461	-0.258070	0.110092	-0.029366	-0.063248	0.117778	0.204283	0.146004	0.007462	-0.019896	-0.132005	-0.044717	0.153674	-0.058143	-0.073183	-0.101065	0.197508	-0.162663	0.074115	0.142624	0.059339	-0.022627	0.020403	-0.338296	-0.045206	0.101116	-0.214144	0.657989	-0.080803	-1.585619	-0.253716	-0.190774	1.196851	0.414516	0.115869	0.570089	0.004932	0.438396	0.387068	-0.002619	0.304828	-0.051145	0.127153	-0.171310	-0.160158	0.157538	0.182794	-0.141734	0.248294	-0.177318	0.047835	0.246789	-0.102835	0.135122	0.316227	0.311679	-0.452130	-0.073952	-0.676841	-0.178936	0.138034	0.029425	-0.077797	0.024991	-0.165579	-0.017992	-0.049247	0.146886	-0.147412	0.107798	-0.093446	-0.186209	0.497690	-0.142333
3	-0.121608	-0.009518	0.091875	-0.170958	0.015582	0.043776	-0.065596	0.160869	-0.102446	-0.042812	0.136753	-0.068365	0.270953	0.055043	0.040918	-0.156060	-0.155206	0.037050	-0.075091	-0.196143	0.292273	-0.049422	-0.017261	-0.021445	0.244321	0.009893	-0.108910	-0.185257	0.200700	-0.028236	-0.060760	0.160254	-0.050513	-0.039509	0.031830	-0.082433	-0.010622	-0.027567	0.146669	0.032890	-0.351559	-0.144244	0.257439	-0.182824	-0.017187	0.123652	-0.031100	-0.131701	-0.118843	-0.304067	0.060921	0.074329	-0.036016	0.619687	-0.013071	-1.210739	-0.124201	0.009311	0.854737	0.337838	-0.049072	0.358958	-0.057553	0.373329	0.296351	0.080542	0.099622	0.131685	0.030505	-0.026552	-0.002022	-0.081624	-0.012779	-0.261384	0.221026	-0.188425	-0.088523	0.061425	-0.118564	-0.033474	0.275439	-0.132491	-0.301339	-0.015219	-0.627419	-0.065667	0.221838	0.115023	0.008114	-0.208594	-0.153191	0.145771	0.015813	0.106705	0.003378	-0.082017	0.002220	-0.115923	0.547772	0.003838
4	-0.110340	0.069489	-0.054471	-0.201789	-0.124489	-0.031738	-0.059528	0.250777	-0.161684	0.104659	0.184042	0.069032	0.129479	-0.035208	0.159494	-0.179804	-0.097654	-0.028180	-0.172909	-0.088832	0.267968	-0.048098	0.087056	0.202160	0.338263	0.073135	-0.147115	-0.309813	0.167016	0.119717	-0.018425	0.165487	0.109064	-0.012114	0.076627	0.101561	0.124484	0.149059	0.076543	0.175569	-0.028395	-0.172859	0.096891	-0.225492	-0.021989	0.004879	0.014807	-0.102056	-0.071461	-0.220515	0.041045	-0.151519	0.047595	0.659195	0.041423	-1.366097	-0.204859	-0.095788	0.953691	0.508999	0.244080	0.324288	-0.014122	0.373400	0.282500	0.106688	0.206739	0.044027	0.159298	-0.083985	-0.191478	0.019235	-0.024839	0.038149	0.255182	-0.069310	0.036995	-0.028285	-0.221641	0.002576	0.285015	0.025489	-0.425243	-0.061976	-0.529828	-0.150613	0.186141	-0.093787	0.025842	-0.101933	-0.052394	0.021247	-0.051975	0.012392	-0.129198	0.076058	-0.054825	-0.214423	0.483402	-0.254611
5	-0.161030	-0.072194	0.020466	-0.093366	-0.072651	0.067558	-0.038169	0.284958	0.163659	0.055468	0.016979	0.114312	0.181107	0.056560	0.195324	-0.071721	-0.055969	0.024129	0.100819	-0.223950	0.035411	0.068302	-0.289909	-0.246565	0.155118	0.328717	-0.218347	-0.111237	0.187756	-0.153271	0.105817	0.096346	0.047467	-0.073990	-0.086066	-0.123889	0.041861	-0.098575	0.148060	0.016376	0.071436	-0.198084	0.010726	-0.167215	0.145264	0.195021	0.018373	-0.153895	-0.120979	-0.339825	0.020231	0.172191	-0.039032	0.677452	-0.153568	-0.982517	0.014443	-0.325216	0.864960	0.208985	0.133095	0.437253	0.127657	0.367165	0.235317	0.050052	0.264245	-0.287446	0.044746	-0.202630	-0.199555	-0.079236	0.235039	0.080779	0.202807	0.125685	0.033481	0.129383	-0.234991	0.113034	0.349035	0.059343	-0.330012	0.212799	-0.787174	-0.154942	0.375196	0.043187	-0.304761	0.086441	-0.046455	0.097607	-0.101911	-0.003314	0.106734	0.024734	0.078975	-0.155226	0.375150	-0.022567
6	-0.052552	0.061222	-0.039139	-0.078893	0.056725	0.232348	0.018869	0.081902	-0.071985	-0.029720	0.061463	0.088999	0.378218	0.102708	0.140834	0.008885	-0.136307	-0.006583	0.133952	-0.315680	0.309430	0.039587	0.068865	0.123116	0.421080	0.067881	-0.408116	-0.132264	0.136119	-0.178350	0.037044	0.095666	-0.122809	0.136791	-0.058879	-0.103740	-0.000025	0.070976	0.129141	0.045806	-0.260319	-0.348326	0.135615	-0.151886	0.042357	-0.082753	-0.055324	-0.090481	-0.070449	-0.499739	0.096615	0.056912	0.039066	0.875434	-0.055412	-1.791234	-0.204507	-0.138615	1.144774	0.437327	0.061191	0.464006	-0.057361	0.399031	0.150013	-0.021491	0.295626	-0.056818	0.071580	-0.115565	-0.223050	0.003752	-0.025399	-0.106008	0.186260	0.021655	-0.013242	0.124430	-0.369944	0.154376	0.573198	0.287466	-0.347110	-0.016344	-0.804869	-0.376052	0.238476	0.028797	-0.059681	-0.097400	-0.164749	-0.117195	-0.003331	0.083677	-0.272668	-0.038568	0.138298	0.000587	0.331572	-0.072223
7	-0.079298	-0.016869	0.154214	-0.075727	-0.007162	-0.173582	-0.035564	0.335806	0.087256	0.252447	0.031281	0.168293	0.178334	0.045631	0.157214	-0.188028	0.027918	-0.048107	-0.005434	-0.177544	0.181488	-0.087801	0.139697	0.052471	0.138293	-0.043503	-0.139721	-0.006301	0.116369	-0.035143	-0.050374	0.226150	-0.073198	0.036256	0.028880	-0.097276	-0.008511	-0.170457	0.284486	-0.100181	-0.080193	-0.144222	-0.100910	-0.378906	-0.097898	0.093800	-0.022395	-0.085072	-0.091723	-0.298120	-0.213138	0.083088	-0.095148	0.619777	0.070941	-0.867351	-0.316103	-0.051028	0.868057	0.219847	0.244803	0.128061	0.011105	0.192394	0.319363	0.064191	0.287455	-0.077200	0.258734	0.020029	-0.257531	0.061814	0.237108	-0.097031	0.241911	-0.021906	0.181040	-0.117301	-0.361589	0.045707	0.215660	-0.097214	-0.488773	-0.075961	-0.399264	0.047063	0.427837	-0.154556	-0.102792	-0.089372	-0.055593	0.098196	-0.079403	0.131417	-0.002185	0.017046	0.004247	-0.208985	0.252978	-0.135947
8	0.099441	0.210626	0.220186	0.128656	-0.247323	0.206204	-0.233702	0.120766	-0.033706	-0.124042	-0.101519	0.157320	0.417461	-0.041147	0.292311	-0.207349	-0.340178	-0.071516	-0.177187	-0.018513	0.068240	0.059270	-0.174082	0.169818	0.118317	0.013854	-0.181528	-0.310645	0.105841	-0.108055	0.231452	0.162682	0.023810	0.041453	-0.163123	0.001526	0.023915	0.065946	-0.014873	0.216735	-0.157451	-0.090496	0.230111	-0.290286	0.322252	0.265296	0.247957	0.095899	-0.118262	-0.657413	0.156360	-0.385319	0.026718	0.839113	0.239743	-1.573550	0.038593	-0.390372	0.966206	0.428061	0.100834	0.565861	-0.131853	0.043967	0.286635	0.075293	0.284802	-0.300589	0.182567	-0.119358	-0.210648	-0.044951	0.019030	-0.049360	0.316549	0.159443	-0.130090	0.021841	-0.440325	0.076917	0.578171	0.196875	-0.249955	0.022816	-0.856527	-0.532092	-0.138015	0.037427	-0.238659	0.006348	-0.048300	-0.079062	0.154441	0.198330	-0.234740	0.137049	0.240098	-0.225954	0.476825	-0.402072
9	-0.013596	0.092831	0.033404	-0.122333	0.092297	0.183141	0.037746	0.233348	0.059000	0.160079	0.244146	-0.036954	0.167959	0.046149	0.175002	-0.120557	0.070199	0.023002	-0.113269	-0.147049	0.212227	0.175153	-0.045346	-0.082362	0.319596	-0.004398	-0.242253	-0.177026	0.097608	-0.110499	0.054577	0.113077	0.040482	0.154525	-0.119433	0.001085	-0.061143	0.190099	0.173400	0.055427	-0.080564	-0.243806	-0.042583	-0.292446	0.015124	0.209598	-0.047581	0.083208	0.061313	-0.235693	0.030057	0.038679	-0.002786	0.804224	-0.087652	-1.445820	-0.235377	-0.078619	1.029515	0.437494	0.063466	0.402628	0.005674	0.261535	0.429057	0.009741	0.246105	-0.096840	0.113444	0.037702	-0.226762	0.027769	0.148152	-0.110049	0.183278	-0.016338	0.094863	0.072010	-0.240294	0.046138	0.264646	0.098339	-0.343301	0.047088	-0.688996	-0.183760	0.223680	-0.091241	-0.140430	-0.055425	-0.080931	-0.062984	-0.004933	0.128233	-0.115109	0.058542	0.054202	-0.041732	0.475065	0.027234
10	-0.219798	0.036565	0.023163	-0.345883	-0.110402	-0.048545	-0.196149	0.255794	-0.029564	0.277364	0.144384	-0.013105	0.243728	-0.046308	0.286196	-0.178552	-0.070633	-0.035131	0.068460	-0.362794	0.080725	-0.003282	0.050257	0.082025	0.316082	0.019774	-0.099223	-0.139246	0.007316	0.219578	0.046456	0.252960	-0.066598	-0.019636	0.096725	-0.001953	0.005402	0.127835	0.142608	0.109844	-0.010549	-0.160529	-0.037387	-0.200197	-0.091432	0.084098	-0.052647	0.003190	-0.100368	-0.207871	-0.150372	0.059845	0.031101	0.640784	0.003907	-1.161894	-0.164628	-0.136455	0.861379	0.364956	0.248478	0.318347	0.009846	0.386771	0.202691	0.042173	0.333878	0.002293	0.057221	-0.177105	-0.042341	0.024190	0.200202	0.171364	0.203607	-0.082339	0.015492	0.080912	-0.104048	0.045408	0.186913	0.036428	-0.363559	0.046066	-0.487034	-0.107383	0.204560	-0.007019	-0.065331	-0.172434	-0.056680	0.025155	-0.064107	-0.077948	0.067712	-0.074471	0.131819	-0.231083	0.714914	-0.018622
11	-0.340736	0.275579	0.114693	-0.025032	-0.146860	0.116122	0.228414	0.342922	-0.043931	0.057679	0.117941	-0.062609	0.070388	0.131110	0.016729	-0.290370	-0.017245	-0.135955	-0.217829	-0.171099	0.040651	0.196071	0.153877	-0.042521	0.012948	0.295347	-0.198863	-0.239749	-0.036736	-0.000575	-0.214169	0.133740	-0.060380	0.008235	-0.103613	0.147050	0.037573	0.243304	-0.162917	-0.085953	-0.400838	-0.359304	-0.093005	-0.487313	0.278021	0.102771	-0.342568	-0.223597	-0.045700	-0.562118	0.178462	0.023811	0.099395	0.775631	-0.187286	-1.509780	-0.090159	-0.077517	1.157086	0.240185	0.044323	0.461150	-0.008212	0.239202	0.540522	0.078167	0.218140	-0.220422	0.118551	-0.190106	-0.142345	-0.135936	0.154027	0.116226	0.122313	0.206125	-0.153431	0.015476	-0.229132	0.108994	0.527788	-0.041642	-0.435112	0.119078	-0.709460	-0.000284	0.107885	0.045327	-0.097880	-0.025434	-0.089855	0.058290	-0.087296	-0.058809	-0.365571	-0.024920	0.032551	-0.048193	0.514849	0.188943
12	-0.161743	-0.029301	0.053498	-0.041645	-0.046404	0.064723	-0.068518	0.205514	0.321204	0.153243	0.141325	-0.022252	0.258887	0.129556	0.172719	-0.079825	-0.019011	0.005628	-0.082073	-0.045916	0.207484	0.049842	-0.012490	-0.048484	0.150864	-0.010882	-0.203194	-0.248015	0.134786	-0.015937	0.200500	0.290362	-0.106703	0.127341	-0.084182	-0.033884	0.109353	-0.006584	0.077096	0.022730	-0.250740	-0.121465	-0.020774	0.000223	0.091749	-0.016673	-0.102751	-0.044677	-0.050624	-0.400469	0.046007	-0.013271	-0.039424	0.640669	-0.121457	-1.450041	0.008680	-0.205854	1.067642	0.242333	-0.052096	0.508761	-0.169928	0.210316	0.491572	-0.027047	0.288116	0.040431	0.145227	-0.219642	0.027143	-0.022779	0.145184	-0.282473	0.279102	-0.007235	0.071573	0.054806	-0.152461	0.094925	0.233482	0.305364	-0.383576	0.077986	-0.772708	-0.143723	0.259249	0.085819	-0.295062	-0.091695	-0.101822	-0.055021	0.001325	-0.051623	-0.041049	-0.048755	0.147516	-0.122424	0.405204	-0.092998
13	-0.157209	0.006237	0.141957	0.130437	0.051307	-0.207963	-0.015998	0.305434	-0.062725	0.000708	0.129817	-0.130909	0.061171	0.002961	0.072002	-0.215291	-0.039476	0.100321	0.061984	-0.035547	0.217910	-0.225317	0.120888	-0.041818	0.306308	-0.081367	-0.040453	-0.022173	0.051371	-0.048389	-0.134090	0.264553	-0.222403	0.007576	0.044839	-0.127215	0.153793	-0.027772	0.079341	-0.129657	-0.184974	-0.359807	0.103511	-0.351621	-0.008670	0.172235	-0.039886	0.006350	-0.066099	-0.507103	0.037114	0.126888	-0.017553	0.731217	-0.036582	-1.629417	-0.191764	-0.095240	1.348342	0.416070	-0.010443	0.261959	0.044164	0.221635	0.466323	0.069725	0.062142	0.309759	-0.100333	-0.179372	-0.023282	-0.111884	0.043026	-0.188480	0.230637	-0.090589	0.084480	0.018065	-0.318328	0.026358	0.502180	-0.195317	-0.333283	0.213945	-0.867842	0.020063	0.226974	0.003104	-0.027986	-0.080947	-0.055552	0.124100	-0.162134	0.079364	-0.126903	0.111678	0.063182	-0.122069	0.572937	-0.108311
14	-0.190663	-0.055745	0.057872	-0.284231	-0.068229	0.015243	-0.141304	0.311732	0.013222	0.172630	0.018359	-0.021563	0.162708	0.071248	0.193310	-0.041378	0.093849	0.000612	0.221411	-0.232856	0.171862	-0.093962	-0.064705	-0.023489	0.388868	0.271250	-0.198094	-0.233074	0.108310	-0.156623	0.115697	0.209882	-0.056457	0.011540	0.061599	0.135217	-0.054795	-0.040512	0.146373	0.146621	0.018979	-0.272177	-0.033226	-0.103522	-0.143964	0.100543	0.014008	0.011650	0.075700	-0.470413	0.065973	0.062806	-0.163589	0.654049	-0.089641	-1.282143	0.012930	0.023920	1.136837	0.346306	0.148130	0.556092	0.008074	0.350797	0.314777	0.059153	0.370637	-0.223140	0.096303	-0.044613	-0.189351	-0.062657	0.214538	0.011361	0.223778	0.113519	0.213778	0.056156	-0.076404	0.065142	0.281920	-0.068059	-0.331319	0.063213	-0.775534	-0.194008	0.195539	-0.075747	-0.249949	0.057544	-0.063366	-0.087540	0.091800	-0.005171	0.027826	-0.000351	0.125883	-0.164736	0.466054	-0.175265
15	-0.228098	0.085892	0.017657	-0.103939	-0.066945	-0.014978	-0.079491	0.162783	-0.052215	0.168757	-0.075655	-0.111422	0.156215	0.185790	0.053770	-0.138109	-0.029959	-0.035754	-0.007707	-0.015181	0.241202	-0.008130	-0.147631	0.042719	0.087990	0.032314	-0.104322	-0.150016	-0.051222	-0.098368	0.130997	0.257116	-0.111212	0.052993	-0.002154	-0.053768	0.114274	0.040176	0.112025	0.028977	-0.256851	-0.241936	-0.077054	-0.164172	0.181552	0.064030	0.101922	0.052307	-0.043533	-0.519411	0.063664	0.051330	-0.017536	0.778616	-0.001345	-1.547805	-0.098313	-0.222583	1.146766	0.263485	0.112883	0.584404	-0.099249	0.263552	0.453479	0.071711	0.281697	-0.184117	0.174213	-0.083570	-0.104829	0.003007	0.125504	-0.142659	0.262675	0.111467	0.237672	-0.074057	-0.366252	0.176109	0.518320	0.057163	-0.326269	-0.083924	-0.815499	-0.210718	0.251458	-0.067480	-0.117665	-0.110936	0.010914	-0.167065	-0.128042	0.027493	-0.199866	0.122420	0.034699	-0.226127	0.399792	-0.174291
16	-0.047245	0.153347	-0.041622	-0.106247	-0.080261	-0.160158	-0.214129	0.245227	-0.024883	0.151453	0.141285	-0.046655	0.272271	0.041657	0.111156	-0.102340	-0.033635	0.000462	-0.079846	-0.203067	0.140142	-0.085488	0.057783	-0.102576	0.345778	-0.017469	-0.197750	-0.224566	0.081285	0.020691	-0.023975	0.210145	0.054111	-0.077058	-0.009529	-0.031506	-0.041336	0.067482	0.181329	0.002351	-0.096606	-0.173410	0.203120	-0.199515	0.019549	0.132726	0.003517	-0.082462	-0.100201	-0.345062	-0.032043	0.025578	0.008477	0.783931	-0.025827	-1.504851	-0.118538	-0.273079	1.076245	0.453975	0.105650	0.273572	-0.068209	0.369913	0.313562	0.017948	0.291842	0.134352	0.052418	-0.205828	-0.033334	-0.068886	0.069151	-0.139218	0.307068	-0.073600	0.118843	0.105404	-0.262309	-0.012056	0.245970	0.022134	-0.297403	0.036401	-0.693978	-0.006367	0.223728	-0.041028	-0.017112	-0.104425	0.011935	-0.068599	-0.094515	0.150368	-0.039004	-0.028620	0.130378	-0.212051	0.502041	-0.090125
17	-0.043219	-0.086491	0.195621	-0.134598	0.224942	-0.152041	0.035424	0.104569	-0.104319	0.145088	-0.045719	0.058181	0.054561	0.224526	0.091649	-0.162692	-0.048062	0.010198	-0.035585	-0.109121	0.237136	-0.204096	0.029707	-0.032732	0.208809	-0.032451	-0.059428	-0.309902	0.130825	0.021893	-0.095654	0.265935	-0.132345	0.105023	-0.058324	0.034020	0.040250	0.025507	0.041355	0.162173	-0.064968	-0.298751	-0.046054	-0.239319	0.261207	0.023548	0.247397	-0.091530	-0.053872	-0.419470	0.034613	-0.212163	-0.119313	0.701831	0.084926	-1.776415	-0.165486	-0.206387	1.106128	0.340252	-0.074044	0.393138	-0.094925	0.152418	0.514824	0.123682	0.343791	0.022862	0.273542	0.003277	0.078631	-0.074626	0.066503	-0.155930	0.135315	0.002852	0.003954	-0.025864	-0.344134	0.062443	0.424812	0.070528	-0.468722	0.159830	-0.759648	-0.124293	0.270947	-0.108905	0.090334	-0.091934	-0.036808	0.013949	-0.108593	-0.046255	-0.326562	0.060964	0.117497	-0.207410	0.619575	-0.319131
18	-0.176465	0.148006	-0.001436	-0.144056	-0.039305	0.071365	0.023372	0.233437	-0.145027	0.042196	0.123373	-0.105170	0.370647	0.259873	0.211365	-0.114318	0.088660	-0.060691	-0.004251	-0.269154	0.333309	0.019259	-0.014131	0.065038	0.386285	0.118460	-0.234352	-0.148081	0.163384	0.076792	0.062880	0.159721	0.005068	0.093866	-0.052335	-0.075280	-0.036576	0.124894	0.019385	0.024641	0.028082	-0.247386	0.109454	-0.199084	0.011965	0.134591	0.055129	0.014017	-0.083559	-0.299531	-0.023610	0.141049	0.002489	0.716487	-0.080901	-1.565442	-0.230317	-0.132270	1.088385	0.438900	0.122303	0.543144	-0.037739	0.436065	0.386110	-0.089954	0.138406	-0.005769	-0.032334	-0.128061	-0.146590	-0.140212	0.132836	-0.027975	0.355546	0.080118	0.149310	0.032404	-0.243853	-0.071670	0.232403	0.010003	-0.331132	0.019021	-0.662587	-0.103665	0.182762	-0.162875	0.004893	-0.064072	-0.179373	-0.063802	-0.064338	0.043386	-0.142725	0.078966	-0.127716	-0.110648	0.571396	-0.042045
19	0.033946	-0.090802	0.091090	-0.049628	-0.001465	-0.039267	-0.289145	0.159869	-0.023683	-0.038881	0.141651	0.177044	0.279737	0.065229	0.216307	-0.083618	0.083830	0.015436	-0.109946	-0.125932	0.186823	-0.077448	0.080218	-0.060162	0.285515	-0.015319	-0.196020	-0.217103	0.083308	-0.127066	0.102240	0.273434	0.086549	0.005013	-0.089606	0.046718	-0.036725	-0.021459	0.126113	-0.029891	-0.160352	-0.173609	0.256060	-0.200312	0.042613	0.065605	-0.090390	0.002457	-0.103971	-0.364179	-0.010618	0.020033	-0.111851	0.830547	-0.126838	-1.518310	-0.250899	0.017860	1.133868	0.295913	0.004563	0.199396	-0.129540	0.245423	0.279551	0.004790	0.250152	0.027958	0.056684	-0.026915	-0.188274	-0.055431	0.041103	-0.261374	0.256209	0.018368	0.127070	0.068130	-0.357074	0.012415	0.326718	-0.017372	-0.329750	-0.024031	-0.678165	-0.109888	-0.067911	-0.116779	0.032172	-0.190952	0.070071	-0.171460	-0.140754	0.058538	-0.252193	-0.087286	0.127572	-0.194893	0.382866	-0.067220
20	-0.060580	0.103441	0.103814	-0.019604	0.088822	-0.015887	-0.048101	0.266675	0.018566	-0.065050	0.235045	-0.068402	0.154739	0.133554	0.039322	-0.047851	0.017409	0.225351	-0.171810	-0.159792	0.196450	-0.104150	-0.020841	0.089406	0.460460	-0.142595	-0.074276	-0.286632	0.152771	-0.095268	-0.247764	0.312588	-0.048118	-0.010712	0.262136	-0.007477	-0.109431	0.211082	-0.125550	-0.087623	-0.070008	-0.411328	0.296024	-0.209120	0.096369	0.199126	0.073493	-0.139748	0.151139	-0.447925	0.009236	0.059514	-0.013901	0.784257	-0.090778	-1.941040	-0.268002	-0.114510	1.369638	0.357489	-0.117105	0.336267	-0.089617	0.264939	0.440342	-0.078883	0.157133	0.155813	-0.022578	-0.102118	-0.194843	0.089282	0.165796	-0.224918	0.163329	-0.025950	0.011957	-0.055385	-0.407632	-0.048875	0.483948	-0.006318	-0.370965	0.293964	-0.885110	-0.222541	0.070124	-0.035366	-0.052959	-0.076584	-0.007375	0.057617	-0.037174	0.192581	-0.154417	0.184742	0.076347	-0.059260	0.478042	-0.093572
21	-0.149279	0.019653	-0.020016	-0.043263	0.030332	-0.115595	-0.051067	0.223154	-0.188176	0.143121	0.234757	-0.176328	0.120315	0.128717	-0.079200	-0.200007	-0.080192	0.052279	-0.155418	-0.168327	0.229303	0.148912	0.014417	-0.098051	0.208119	0.008215	-0.289078	-0.273099	0.004712	-0.094657	-0.055989	0.284212	-0.072558	0.086138	-0.070972	0.031304	-0.036886	0.048773	0.139546	-0.081967	-0.050512	-0.411289	-0.000768	-0.253741	0.092802	0.028588	-0.190076	-0.065042	-0.085896	-0.358573	-0.045105	0.029304	-0.087355	0.825029	-0.085493	-1.366590	-0.161405	-0.096212	1.145236	0.420705	0.094537	0.142858	0.129605	0.398786	0.255741	0.145184	0.264197	0.250458	0.150082	0.038071	-0.152770	-0.050074	0.222287	-0.007465	0.122912	-0.166348	-0.028368	-0.021914	-0.176281	-0.170069	0.376212	-0.292672	-0.301631	-0.061952	-0.626602	-0.127808	0.382757	-0.048168	0.199838	-0.226323	-0.142871	0.134816	0.002968	0.162240	0.055237	-0.007963	-0.066306	-0.062973	0.668921	-0.184750
22	-0.064475	-0.161965	0.239039	0.085443	0.043962	-0.049040	-0.202260	-0.059787	-0.045402	0.129146	0.007316	-0.226177	0.172414	0.028523	0.226979	-0.079792	-0.163262	-0.097465	-0.002557	-0.214707	0.174484	-0.079769	-0.045883	-0.003187	0.224368	-0.037358	0.039999	-0.287769	0.169680	-0.110527	-0.048648	0.160854	-0.131790	0.025173	-0.024531	-0.224794	0.052578	0.076145	0.017704	-0.156009	-0.292980	-0.204728	0.036119	-0.154695	-0.099769	0.036898	0.088382	-0.041323	-0.054813	-0.362499	-0.010077	-0.001850	-0.055217	0.778091	0.064261	-1.467680	-0.027068	-0.285256	0.948683	0.245417	-0.002839	0.231227	-0.219904	0.167957	0.456027	0.120578	0.178478	0.257507	0.104234	0.121474	-0.016890	-0.082948	0.010160	-0.143754	0.208110	-0.105213	0.011257	0.055961	-0.222625	0.008408	0.248348	-0.027617	-0.403106	0.097279	-0.680743	0.065928	0.099821	-0.030832	0.002924	-0.291781	-0.328140	-0.006867	-0.235273	0.257583	-0.153034	-0.030837	0.077353	-0.143178	0.505832	-0.002949
23	-0.132105	0.002016	0.193841	-0.185131	0.019069	-0.143982	-0.098563	0.083463	0.025443	0.215244	0.215880	-0.180434	0.323793	0.130523	0.030374	-0.243082	-0.173406	0.081238	-0.025983	-0.199628	0.201827	-0.081906	0.139101	-0.061751	0.434229	-0.005798	-0.336718	-0.174172	0.139063	0.127645	0.219424	0.428461	-0.262912	-0.068894	0.006520	-0.075968	-0.010072	-0.030781	0.140105	0.081238	-0.113167	-0.219432	0.216687	-0.213032	-0.114257	0.070576	0.148243	-0.080134	0.101634	-0.565498	-0.093252	0.185619	-0.090388	0.826486	0.214719	-1.455717	-0.123719	-0.021697	1.281690	0.534219	0.216648	0.168191	0.026906	0.666664	0.341614	0.029729	0.127137	-0.041537	-0.045463	-0.058499	-0.072161	-0.110795	0.091837	-0.031444	0.344427	-0.036503	0.219898	0.031370	-0.273675	0.159219	0.508572	-0.144660	-0.373627	0.041441	-0.762438	0.051845	0.381865	0.001046	-0.286409	-0.273200	-0.008780	-0.021735	-0.105295	-0.087843	-0.017160	-0.112776	0.061972	-0.215139	0.741063	-0.180484
24	-0.191711	0.150163	0.089809	-0.000494	0.053982	0.001466	-0.161406	0.203217	-0.032318	-0.048251	0.270398	0.053245	0.181413	0.068842	0.028136	-0.035522	0.030485	-0.031243	-0.025178	-0.192938	0.112737	0.084719	-0.018527	-0.006313	0.237691	0.029599	-0.171940	-0.209572	0.053348	-0.089741	0.010349	0.185826	-0.037038	0.155954	-0.049043	0.114760	0.054396	0.070351	0.010194	-0.241138	-0.145887	-0.273753	0.100435	-0.243409	-0.060708	0.106341	-0.210611	0.045208	-0.015325	-0.323509	-0.016323	0.063681	0.009917	0.759583	-0.142388	-1.441045	-0.206079	-0.142625	1.076081	0.330718	-0.106155	0.307783	-0.170156	0.282285	0.397897	0.012534	0.239076	0.201713	0.012400	-0.179563	-0.173967	0.010270	0.131638	-0.101033	0.210793	0.097198	-0.021526	0.107876	-0.215362	-0.130169	0.305421	0.063644	-0.391355	-0.003943	-0.711220	-0.151955	0.210921	0.017586	-0.030445	-0.112835	-0.130591	0.043765	-0.015891	0.161469	0.009404	-0.020130	0.121311	-0.053643	0.399410	0.085081
25	-0.251551	0.251361	-0.058960	-0.104333	0.051185	-0.101678	-0.083734	0.244903	-0.044702	0.145015	-0.013100	-0.163864	0.437663	0.240141	0.130115	-0.103658	0.109712	-0.088026	0.231336	-0.297243	0.310683	0.034893	-0.062337	0.040692	0.380165	0.070797	-0.156953	-0.284794	0.047521	-0.028707	0.145921	0.340856	-0.118922	0.045204	0.083554	0.069049	-0.057300	0.033121	0.108003	-0.228110	-0.032914	-0.176763	0.029961	-0.201627	-0.134957	0.010180	-0.144875	-0.223594	-0.081127	-0.381479	-0.106180	0.171497	-0.006147	0.778109	-0.100627	-1.133197	-0.229572	-0.176154	1.122276	0.200638	0.224902	0.314323	-0.133282	0.345715	0.468857	-0.204036	0.388722	0.123183	-0.039391	-0.105422	-0.084581	0.018233	0.161410	0.003778	0.262003	0.068295	-0.018703	0.048196	-0.147756	0.109930	0.298015	0.201035	-0.308865	0.180065	-0.811070	-0.048531	0.358357	0.080422	-0.044131	-0.168317	-0.121328	-0.126442	0.029604	-0.074935	-0.097140	-0.079109	-0.008512	-0.220272	0.504390	-0.158581
26	-0.058602	0.111019	0.362054	-0.188750	-0.135637	0.212612	-0.153337	0.166948	-0.104605	-0.164346	0.224253	-0.006274	0.244169	0.088919	0.118354	-0.166723	-0.072567	0.084619	-0.281149	-0.155447	0.171146	0.089804	0.029481	-0.186808	0.167928	-0.110797	-0.345942	-0.427670	0.137076	-0.171046	-0.203329	0.317063	-0.021752	0.054102	0.120584	0.180543	-0.144185	0.230600	-0.019698	0.031005	-0.155726	-0.267186	0.268096	-0.277834	-0.025951	0.033233	-0.040053	-0.056497	0.067004	-0.609144	0.174440	0.051946	-0.086387	0.840842	-0.039690	-2.062701	-0.153493	-0.249064	1.344577	0.387633	-0.166555	0.576075	-0.004072	0.162385	0.473422	0.079335	0.386651	0.065451	-0.004348	-0.008851	-0.156333	-0.122766	0.093210	-0.307755	0.271636	0.127379	-0.229714	0.076574	-0.308714	0.018902	0.277966	0.122855	-0.421393	0.005396	-1.020117	-0.123319	0.081140	0.016061	-0.097731	-0.141194	-0.169562	-0.169816	0.029651	0.053033	-0.402551	-0.002704	0.037741	-0.113324	0.393972	0.101089
27	-0.163030	0.147055	0.160617	-0.130910	-0.107540	0.059077	-0.139623	0.170636	0.161982	0.105290	0.215406	0.062255	0.165770	0.065339	0.184489	-0.125840	0.138193	0.018979	0.000551	-0.081577	0.100474	-0.136379	0.044481	-0.051629	0.443831	-0.011362	-0.159527	-0.202300	0.102585	-0.146120	-0.124219	0.087795	-0.085686	0.120656	0.105640	-0.015516	-0.074860	-0.131586	0.104731	-0.065716	-0.091310	-0.149990	0.026613	-0.041985	-0.055483	0.040741	0.033619	-0.047859	0.113204	-0.343655	0.069466	0.102462	-0.087244	0.706412	-0.087247	-1.534807	-0.127054	-0.215288	1.097679	0.379264	-0.046594	0.368818	-0.011710	0.374794	0.413692	0.096718	0.262356	-0.073248	-0.089269	-0.257844	-0.106516	-0.087849	0.125328	-0.088275	0.224530	-0.333000	0.165423	0.010659	-0.137293	-0.163611	0.298408	-0.011917	-0.350305	0.078234	-0.627775	0.003555	0.101470	-0.117847	-0.071659	0.089351	-0.081486	0.047771	-0.180567	0.121275	-0.022006	-0.146952	0.049733	-0.143076	0.508795	0.074684
28	-0.163957	0.260097	-0.069616	-0.416195	0.067422	0.147737	-0.209080	0.133392	-0.147697	0.265763	0.132996	-0.078895	0.242780	0.200509	0.129654	0.109269	-0.061151	0.067830	-0.191332	-0.226332	0.296061	0.148274	0.112252	0.050589	0.474381	-0.020386	-0.249216	-0.406259	0.095676	-0.228850	-0.078391	0.225760	0.129881	0.029060	0.130985	0.001240	-0.084381	0.039574	-0.087026	-0.127831	-0.171980	-0.284887	0.045360	-0.094461	-0.029359	0.006247	-0.170961	-0.073036	-0.063992	-0.583220	-0.106464	0.102099	-0.139138	0.996781	-0.100852	-1.720202	-0.278450	-0.110761	1.500463	0.496373	0.048849	0.530582	-0.264116	0.298515	0.438406	0.076038	0.235391	0.032547	-0.042904	-0.290205	-0.064321	0.041795	0.176880	-0.162809	0.274585	-0.019271	-0.025926	0.019028	-0.097040	0.194505	0.427528	0.204592	-0.497070	-0.015804	-0.917337	-0.305015	0.341749	-0.010383	-0.196030	-0.015355	-0.151321	-0.184127	-0.159798	0.041470	-0.229072	-0.189466	-0.014352	-0.204946	0.523140	-0.063884
29	-0.225621	-0.050762	0.041110	-0.189655	-0.008254	-0.099681	-0.119750	0.072225	0.102049	0.049369	-0.007288	0.025826	0.072888	0.126317	-0.194303	0.172341	-0.185230	0.065742	-0.175474	-0.098786	0.109884	-0.174504	-0.066022	-0.114806	0.243558	0.073800	-0.252184	-0.100329	0.004113	0.029887	0.131029	0.246817	-0.095537	0.172685	-0.187793	-0.056063	-0.052498	-0.113416	0.152004	0.012907	-0.390258	-0.268203	0.305635	-0.242192	-0.414075	0.149467	-0.204177	0.043061	0.100178	-0.341351	0.171132	0.259485	0.057309	0.794947	-0.253032	-1.175293	-0.175108	-0.283316	0.926756	0.276350	-0.049413	0.302040	0.029860	0.374140	0.474110	-0.079418	0.162448	0.180994	0.024107	0.039551	-0.026861	0.090757	-0.145639	-0.293709	-0.027480	-0.144225	0.069738	-0.097169	-0.323992	0.212375	0.477398	0.014575	-0.273762	-0.264876	-0.696175	-0.096631	0.335724	0.241046	0.032534	-0.238136	-0.249271	0.253542	-0.399593	-0.128718	0.279142	-0.105670	0.102807	-0.087665	0.459998	0.007292
30	-0.073825	0.165197	0.121121	-0.131877	-0.010120	-0.059892	-0.209315	0.258799	-0.006138	0.117476	0.220896	-0.018270	0.289034	0.038950	0.128151	-0.056150	-0.113186	0.106838	0.148730	-0.293446	0.281320	0.043020	-0.042209	0.065419	0.371553	0.077926	-0.148110	-0.110144	0.082702	-0.027104	-0.112578	0.124257	0.025192	0.032176	0.047307	-0.060060	-0.000387	0.165252	-0.077789	0.103950	0.036205	-0.316979	0.139466	-0.326567	0.124310	0.017096	0.067330	0.014973	0.027031	-0.305237	0.034513	0.064901	0.020527	0.677838	-0.052080	-1.531278	-0.331791	-0.180115	1.119278	0.272132	0.086915	0.278235	-0.022822	0.445619	0.289462	0.040257	0.304091	-0.128597	-0.095264	-0.238061	-0.124832	0.038881	0.198820	-0.080520	0.281888	0.099265	-0.023389	0.072084	-0.180249	0.172549	0.274395	-0.002914	-0.376562	0.123157	-0.645352	-0.166219	0.247551	0.102936	-0.063609	0.082364	-0.129060	-0.015345	-0.027486	0.189103	-0.149053	-0.034262	0.186961	-0.124879	0.580651	-0.091268
31	-0.072841	0.095273	0.090346	-0.062299	-0.007289	0.137294	-0.101673	0.161605	-0.075082	0.137879	0.207695	0.021414	0.291271	0.088103	0.031023	-0.113164	0.128592	0.016132	-0.106965	-0.108280	0.170710	0.110003	0.116042	-0.045747	0.251432	-0.136173	-0.295728	-0.300382	0.192829	-0.136658	-0.008663	0.280377	-0.067945	0.099983	0.052754	-0.040092	-0.111216	0.131919	-0.000431	-0.086583	-0.182612	-0.198355	0.223991	-0.220588	-0.079410	-0.009554	-0.071317	-0.162213	-0.008347	-0.569591	0.027641	-0.000754	-0.073749	0.927800	0.015805	-1.808914	-0.133063	-0.193413	1.395798	0.506710	0.020800	0.518208	-0.139455	0.300597	0.474147	-0.078685	0.324598	0.159578	-0.009290	-0.136255	-0.148927	0.031268	0.016598	-0.210540	0.192264	0.094090	-0.043481	-0.005459	-0.355400	0.004171	0.422168	0.031445	-0.295348	-0.048664	-0.969057	-0.200723	0.230333	0.061191	-0.157232	-0.122542	-0.098070	-0.163837	0.023234	0.136620	-0.245646	-0.026202	-0.066553	-0.170131	0.518651	-0.089393
32	-0.116383	0.022618	-0.047778	-0.160453	-0.004772	0.003285	-0.269881	0.150749	0.034476	0.116953	0.213054	-0.062816	0.344229	-0.073937	0.084249	-0.067871	0.028451	-0.070072	-0.061568	-0.212642	0.223157	0.102364	-0.030911	-0.025746	0.259500	0.108986	-0.209044	-0.386804	0.170134	0.095803	-0.121206	0.243472	-0.058236	0.008423	0.169660	-0.140203	-0.149422	0.116785	0.137866	-0.075043	-0.127082	-0.141326	0.199341	-0.128579	0.000909	0.204929	-0.250633	-0.067108	-0.144797	-0.421643	0.066568	0.027443	-0.224129	0.873353	-0.096926	-1.373453	-0.255623	-0.166738	1.337962	0.404961	0.135302	0.361025	-0.082455	0.304659	0.322507	-0.030740	0.288886	0.025486	0.078893	-0.068400	-0.127742	-0.026755	0.019988	-0.193673	0.438493	0.064682	0.066640	0.099832	-0.257302	0.114998	0.330119	0.091161	-0.417272	0.131068	-0.837963	-0.041545	0.284411	-0.013225	-0.211534	-0.174913	-0.030723	-0.192896	-0.091696	0.013658	-0.090340	0.021278	0.148865	-0.242730	0.605975	-0.181560
33	-0.040007	-0.093044	0.037188	-0.019698	-0.099216	-0.080974	0.085278	0.248616	-0.036073	-0.047933	0.314116	-0.014289	0.213893	-0.009946	0.038843	-0.269457	-0.044659	0.030227	-0.172706	0.131116	0.120657	-0.050474	-0.074995	-0.058372	0.164651	-0.075443	-0.012864	-0.201680	0.202831	-0.063146	-0.175534	0.417183	-0.148218	0.056258	0.129212	0.075637	0.053386	0.217657	0.081558	-0.120252	-0.218609	-0.184573	0.193152	-0.178643	0.063411	0.186890	-0.138213	-0.025678	0.012471	-0.287471	0.002564	0.083417	-0.004735	0.608906	-0.101972	-1.431962	-0.259994	0.008837	0.869670	0.318962	-0.019074	0.140349	-0.133110	0.140304	0.379647	0.096170	0.062532	0.290957	0.070230	0.136336	0.051295	-0.001153	-0.082338	-0.292459	0.232926	-0.000679	0.040399	0.025300	-0.460583	-0.119216	0.314411	-0.195461	-0.252048	-0.054961	-0.569653	-0.228585	0.124625	-0.052169	0.027527	-0.139045	0.068353	0.029648	0.050426	0.254441	0.048014	0.111457	-0.187021	-0.028605	0.295546	-0.133415
34	-0.101814	0.054307	0.029242	-0.176536	-0.100328	0.164570	-0.232781	0.009962	0.188339	-0.034356	0.001299	0.052884	0.387186	0.105282	0.102754	-0.106736	-0.061910	0.025494	-0.001219	-0.194843	-0.070393	0.261606	-0.169114	-0.146509	0.309030	0.181309	-0.307738	-0.205106	0.154151	-0.084521	0.197293	0.121548	0.167163	0.044876	-0.101679	-0.141883	-0.186891	0.103881	0.210666	0.253369	0.070743	-0.082843	0.127746	-0.412092	-0.192047	0.245436	0.064894	0.075481	-0.078964	-0.398332	0.048707	-0.030311	-0.155610	0.572333	0.018689	-1.361315	-0.028022	-0.265919	1.068475	0.595852	0.303969	0.472151	-0.064502	0.474842	0.299868	-0.053861	0.441921	-0.143766	0.113965	-0.311418	-0.295929	0.162429	0.174969	-0.084142	0.333838	-0.207457	0.167627	0.060021	-0.239429	0.135997	0.388327	0.107914	-0.275039	0.002049	-0.750192	-0.244569	0.291082	0.281653	-0.101662	-0.111612	-0.048000	0.073514	-0.158145	0.097626	0.099667	0.126743	0.342251	-0.165784	0.392013	-0.156794
35	-0.141903	-0.032401	0.107782	-0.134803	0.060069	-0.078097	-0.069730	0.250305	0.054792	0.055404	0.137660	-0.074762	0.197579	0.131646	0.042955	-0.249897	-0.170082	0.017949	0.080967	-0.410803	0.007402	0.108613	-0.148073	0.107199	0.260220	0.019452	-0.140405	-0.162834	0.166054	-0.011898	0.027988	0.206309	0.021168	0.154532	-0.074386	-0.048928	-0.143939	-0.078483	0.033154	0.117942	-0.120334	-0.222044	0.152508	-0.204823	0.043214	0.100256	0.070212	-0.137014	-0.138128	-0.366599	0.106443	0.193272	0.062602	0.630227	0.003351	-1.286229	0.033759	-0.169234	0.939029	0.368865	0.183981	0.318398	0.070655	0.440766	0.379962	0.061221	0.193073	0.076972	0.085357	-0.255859	0.002639	0.066562	0.165491	-0.048009	0.205066	-0.136474	0.053613	0.135263	-0.202962	0.088302	0.394963	0.033621	-0.482515	0.178503	-0.676504	-0.064623	0.253011	0.064723	-0.087027	-0.121990	-0.091348	-0.064684	-0.117278	0.025373	0.003190	0.046092	0.108287	-0.158318	0.543111	-0.147611
36	-0.202427	-0.010275	-0.003488	-0.068137	-0.143176	0.025121	-0.079646	0.272078	-0.137211	0.211129	0.168949	-0.033409	0.237357	0.120617	0.153685	-0.039043	0.055725	-0.053934	-0.157074	-0.189956	0.280752	0.090771	0.127665	-0.109661	0.375867	-0.004629	-0.250485	-0.322068	0.059226	-0.053736	0.038532	0.326662	0.177658	0.051816	0.175334	-0.018316	0.017756	0.199237	0.085198	0.121229	-0.009873	-0.365358	0.227723	-0.333911	-0.125979	-0.080153	-0.025185	-0.052805	-0.099508	-0.310186	-0.011066	0.012755	-0.020021	0.918656	-0.164156	-1.618835	-0.116288	-0.175816	1.325732	0.427191	-0.030098	0.387859	0.014746	0.493135	0.393225	0.092034	0.300531	0.086394	0.013131	-0.066830	-0.137062	-0.051100	0.238996	-0.019064	0.168746	-0.032161	0.016774	-0.055501	-0.245191	-0.139431	0.278007	-0.079092	-0.320667	-0.072471	-0.743001	-0.162959	0.238565	-0.020527	0.156754	-0.088507	-0.055101	0.047617	-0.107131	0.075484	-0.125939	0.095654	-0.094874	-0.121560	0.598949	-0.146837
37	-0.073226	0.015102	0.129687	-0.166810	-0.025050	-0.074009	-0.127106	0.265053	0.130588	0.015253	0.020612	-0.100939	0.202298	-0.108913	0.187566	-0.258221	-0.046242	0.065790	0.062817	-0.046225	0.207699	0.151936	0.037514	-0.024995	0.209805	-0.099353	-0.066180	-0.056676	0.076093	0.028015	0.031807	0.352203	0.039374	0.043828	-0.035411	-0.169204	0.007377	0.032955	0.201830	-0.213131	-0.047899	-0.113617	0.071847	-0.154245	-0.036713	0.115077	0.037689	0.034933	0.195864	-0.423273	-0.077460	0.128837	0.046344	0.733940	0.047464	-1.308492	-0.246068	-0.076285	1.347442	0.335846	0.061135	0.305172	0.036977	0.220365	0.453669	0.128240	0.310955	-0.032741	0.162136	0.005017	-0.171233	-0.265781	0.062947	-0.221643	0.239071	0.162488	0.410180	0.001400	-0.465740	0.042629	0.310671	-0.049718	-0.452377	0.161415	-0.827878	-0.096739	0.275805	-0.189190	-0.240080	-0.180633	-0.051844	-0.052933	-0.109073	0.055071	-0.201743	-0.024876	-0.046822	-0.114537	0.577115	-0.041131
38	-0.073226	0.015102	0.129687	-0.166810	-0.025050	-0.074009	-0.127106	0.265053	0.130588	0.015253	0.020612	-0.100939	0.202298	-0.108913	0.187566	-0.258221	-0.046242	0.065790	0.062817	-0.046225	0.207699	0.151936	0.037514	-0.024995	0.209805	-0.099353	-0.066180	-0.056676	0.076093	0.028015	0.031807	0.352203	0.039374	0.043828	-0.035411	-0.169204	0.007377	0.032955	0.201830	-0.213131	-0.047899	-0.113617	0.071847	-0.154245	-0.036713	0.115077	0.037689	0.034933	0.195864	-0.423273	-0.077460	0.128837	0.046344	0.733940	0.047464	-1.308492	-0.246068	-0.076285	1.347442	0.335846	0.061135	0.305172	0.036977	0.220365	0.453669	0.128240	0.310955	-0.032741	0.162136	0.005017	-0.171233	-0.265781	0.062947	-0.221643	0.239071	0.162488	0.410180	0.001400	-0.465740	0.042629	0.310671	-0.049718	-0.452377	0.161415	-0.827878	-0.096739	0.275805	-0.189190	-0.240080	-0.180633	-0.051844	-0.052933	-0.109073	0.055071	-0.201743	-0.024876	-0.046822	-0.114537	0.577115	-0.041131
39	-0.227923	0.194772	0.190770	-0.134514	-0.038846	0.010440	0.100247	0.290690	0.159285	0.118141	0.159152	-0.122386	0.113992	0.228287	0.143530	-0.290608	0.216044	0.109252	0.012200	-0.159551	-0.088545	0.002825	0.011591	-0.147080	-0.044860	0.085009	-0.218937	-0.233285	-0.140662	-0.078638	0.039906	0.183757	-0.326967	0.039005	0.021535	0.161934	0.089920	0.109644	-0.027610	-0.188146	-0.091641	-0.418923	-0.085149	-0.487511	0.129042	0.243938	-0.260780	-0.033369	-0.078915	-0.561320	0.253884	0.160676	0.029951	0.786980	-0.099645	-1.546564	-0.239131	-0.129665	1.464057	0.112059	0.097920	0.410698	0.052807	0.394536	0.506742	0.067851	0.382883	-0.195203	0.172485	-0.253824	-0.138567	-0.187674	0.199766	0.013026	0.113107	0.216128	-0.034754	-0.078417	-0.457728	0.068484	0.606922	-0.081461	-0.483969	0.210306	-0.923541	-0.077506	0.413664	0.033753	-0.218547	-0.043034	-0.054343	-0.071476	-0.084693	-0.104135	-0.251608	0.061095	-0.138828	-0.158671	0.555816	0.078887

	count
Accident Level
I	316
III	303
II	300
IV	300
V	287

	count
Accident Level
I	243
II	242
V	241
IV	240
III	240

	approximately	area	assistant	auxiliary	bar	beam	begin	bolt	bounce	causing	circumstance	climb	cm	collaborator	come	coming	company	cx	described	directed	direction	distance	doctor	drill	drilling	emergency	end	equipment	fall	finger	foot	hand	head	high	hit	hitting	hospital	identified	immediately	impromec	injured	injury	instep	intermediate	jumbo	key	kg	later	leaving	left	length	level	lever	located	locked	loosen	machine	made	maid	maintenance	mechanic	metatarsal	meter	moment	motor	mr	note	nv	ob	one	palm	part	performed	personnel	pick	platform	point	pressure	prevent	proceed	proceeding	proceeds	pull	pulley	pump	reach	removal	removing	right	rock	rod	safety	shoe	slide	solution	station	steel	supervisor	support	task	three	tip	tool	two	unlocking	use	weight	work	worker
0	0	0	0	0	3	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	1	0	1	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	2	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	2	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0	0	2	1	0	0	1	0	0	1	0	0	0	1	3	0	0	0	0	0	0	0	0	0	0
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	0	0	0
2	0	1	0	0	0	0	0	0	1	1	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	2	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	1	1	0	0	0	0	1	1	0	0	0	0	1	0
3	1	0	1	1	0	0	1	4	0	1	0	1	0	1	0	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	2	1	0	0	1	0	1	0	0	0	1	0	0	0	2	0	0	1	1	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	1	0	1	0	0	1	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0
4	1	0	0	0	0	0	0	0	0	1	1	0	2	0	1	0	1	0	1	0	0	1	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	1	0	0	0	1	1	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	2	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1

Industrial safety. NLP based Chatbot¶

Input: Context and Dataset¶

Import necessary libraries and mount google drive¶

Mount Google Drive¶

Milestone 1¶

Process:¶

Step 1: Import the data¶

Step 2: Data cleansing¶

Cleaning null values, duplicates, typos¶

Inferences from above data cleaning¶

Exploratory Data Analysis (EDA)¶

Univariate analysis¶

Inferences from Univariate analysis¶

Bi-variate analysis¶

Multivariate analysis¶

Step 3: Data preprocessing (NLP Preprocessing techniques)¶

Import/download necessary packages¶

Removing Special Characters¶

Change to Lowercase¶

Removing extra whitespace¶

Removing numbers¶

Removing stop words¶

Tokenize¶

Lemmetization¶

Vectorization¶

Word2Vec Embedding¶

Glove Embedding¶

TF-IDF Embedding¶

Step 4: Data preparation - Cleansed data in .xlsx or .csv file¶

Label Encoding¶

One Hot Encoding-Dummies¶

Dataframe column clean-up: Final Dataframe¶

Step 5: Design train and test basic machine learning classifiers¶

X and Y from this session¶

X and Y from Imported from dataset¶

Splitting data¶

Define Scaling, PCA and sampling functions¶

Datasets for w2v¶

Datasets for Glove¶

Datasets on TFIDF¶

Model building and training¶

Accident Level as Target¶

Potentail Accident level as Target¶

Hyper Parameter Tuning¶

Best Model¶

Milestone 2¶

Inputs and Libraries¶

Define functions for split¶

Reading the feature/embedded data¶

Preparing X and Y¶

ANN¶

Reset and Set Data for Collection¶

NN Model On W2V¶

NN Model On Glove Data¶

NN model On TFIDF Data for Basic¶

Model With Layers, Droputs and Normalization - SGD¶

On W2V Dataset¶

On Glove Dataset¶

On TFIDF Datas¶

Model With Layers, Droputs and Normalization - Adam¶

Model with layers,dropout and normalization (Adam optimizer) on W2V, Glove and TFIDF Basic Data¶

Print Results observed so far¶

Hyper parameter on ANN¶

W2V¶

TFIDF¶

RNN¶

RNN (no hyperparameterization)¶

W2V¶

TFIDF¶

RNN (With Parameterization)¶

LSTM¶

Collating Results¶

Data Augmentation¶

Loading packages and data¶

Augmenting Data¶

NLP Processing¶

Load Augmented and NLP processed dataset¶

Embedding Data¶

Define functions¶

LSTM on augmented data¶

	approximately	area	assistant	auxiliary	bar	beam	begin	bolt	bounce	causing	circumstance	climb	cm	collaborator	come	coming	company	cx	described	directed	direction	distance	doctor	drill	drilling	emergency	end	equipment	fall	finger	foot	hand	head	high	hit	hitting	hospital	identified	immediately	impromec	injured	injury	instep	intermediate	jumbo	key	kg	later	leaving	left	length	level	lever	located	locked	loosen	machine	made	maid	maintenance	mechanic	metatarsal	meter	moment	motor	mr	note	nv	ob	one	palm	part	performed	personnel	pick	platform	point	pressure	prevent	proceed	proceeding	proceeds	pull	pulley	pump	reach	removal	removing	right	rock	rod	safety	shoe	slide	solution	station	steel	supervisor	support	task	three	tip	tool	two	unlocking	use	weight	work	worker
0	0	0	0	0	3	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	1	0	1	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	2	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	2	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0	0	2	1	0	0	1	0	0	1	0	0	0	1	3	0	0	0	0	0	0	0	0	0	0
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	1	0	0	0
2	0	1	0	0	0	0	0	0	1	1	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	2	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	1	1	0	0	0	0	1	1	0	0	0	0	1	0
3	1	0	1	1	0	0	1	4	0	1	0	1	0	1	0	1	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	2	1	0	0	1	0	1	0	0	0	1	0	0	0	2	0	0	1	1	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	1	0	1	0	0	1	0	1	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0
4	1	0	0	0	0	0	0	0	0	1	1	0	2	0	1	0	1	0	1	0	0	1	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	1	0	0	0	1	1	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	2	1	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1